mp2i-vms.fr
added amplitude detection and tweaked some parameters
This commit is contained in:
parent
7615d41c01
commit
64a2a96628
761
cleaned_sp.py
761
cleaned_sp.py
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@ -1,13 +1,3 @@
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from math import *
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import numpy as np
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import scipy as scp
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from scipy.io import wavfile
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import matplotlib.pyplot as plt
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import subprocess
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import heapq
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from pathlib import Path
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from time import sleep
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def is_data_stereo(raw_global_data:list) -> bool:
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"""
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self-explainatory
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@ -20,11 +10,15 @@ def is_data_stereo(raw_global_data:list) -> bool:
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return True
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return True
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def dist_to_integer(x):
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ent = np.floor(x+0.5)
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return np.abs(x - ent)
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def is_note_within(fr1, fr2):
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if(fr1 > fr2):
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return (fr1/fr2 <= NOTE_DIST)
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return (fr1/fr2 <= NOTE_DIST and dist_to_integer(fr1/fr2) >= OCTAVE_DIST)
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else:
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return (fr2/fr1 <= NOTE_DIST)
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return (fr2/fr1 <= NOTE_DIST and dist_to_integer(fr2/fr1) >= OCTAVE_DIST)
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def keep_highest(song_name, offset, songlen, segsize, count, output_name, minfreq=110, maxfreq=5000, ampthr=250):
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# extracting data from cropped song
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@ -113,9 +107,83 @@ def keep_highest(song_name, offset, songlen, segsize, count, output_name, minfre
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new_freqs.append(pfreq[elements[idx][0]])
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new_ampls.append(fft_list[i][elements[idx][0]])
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'''for i in range(len(new_freqs)):
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while(new_freqs[i]>1000):
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new_freqs[i] = new_freqs[i]/2'''
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# -------------------------------------------- Get amp distribution -------------------------------------------- #
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new_new_amps = [0 for i in range(int(sample_rate*songlen))]
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new_new_t = [offset + i/sample_rate for i in range(int(sample_rate*songlen))]
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amp_ct = 0
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incr_a = segsize*4
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len_seg_a = int(sample_rate*incr_a)
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count_a = len_seg_a//1250
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left_0 = int(sample_rate*(amp_ct+offset))
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while(amp_ct < songlen-segsize):
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left = int(sample_rate*(amp_ct+offset))
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right = int(sample_rate*(amp_ct+offset + incr_a))
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#returns a list of couples [id, value]
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elements = heapq.nlargest(count_a, enumerate([song_data[i] for i in range(left, right)]), key=lambda x: x[1])
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amp_ct += incr_a
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for idx in range(len(elements)):
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new_new_amps[elements[idx][0]+left-left_0] = song_data[left+elements[idx][0]]
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mmxx = max(new_new_amps)
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new_new_amps = [nnw*1000/mmxx for nnw in new_new_amps]
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# localize peaks
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left_id = 0
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right_id = 0
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a_ampl = 0
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in_seg = False
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time_d = 0.035
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cur_t = 0
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locs = []
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loct = []
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for i in range(len(new_new_amps)):
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if(new_new_amps[i] > 100):
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if(not in_seg):
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in_seg = True
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left_id = i
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right_id = i
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a_ampl = max(a_ampl, new_new_amps[i])
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cur_t = 0
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else:
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cur_t += 1/sample_rate
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if(in_seg and cur_t >= time_d):
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in_seg = False
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locs.append(a_ampl)
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loct.append((left_id + right_id)/(2*sample_rate) + offset)
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a_ampl = 0
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# detect sliders
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sl_a = []
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sl_t = []
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in_slider = False
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slider_dtct = segsize
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for i in range(1, len(loct)-1):
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delta = loct[i] - loct[i-1]
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delta2 = loct[i+1] - loct[i]
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if(delta < slider_dtct and delta2 < slider_dtct):
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if(in_slider):
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sl_t.append(loct[i])
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sl_a.append(locs[i])
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else:
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in_slider = True
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sl_t.append(loct[i-1])
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sl_a.append(locs[i-1])
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sl_t.append(loct[i])
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sl_a.append(locs[i])
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else:
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in_slider = False
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plt.plot(new_new_t, new_new_amps, "b-")
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plt.plot(loct, locs, "ro")
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plt.plot(sl_t, sl_a, "go")
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plt.grid()
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plt.show()
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# -------------------------------------------- Localize -------------------------------------------- #
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@ -136,6 +204,7 @@ def keep_highest(song_name, offset, songlen, segsize, count, output_name, minfre
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plt.plot(new_times, new_kept, "ro")
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plt.grid()
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plt.show()
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# -------------------------------------------- Write -------------------------------------------- #
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i0 = 0
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timing_points.append(999999)
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@ -186,6 +255,8 @@ def keep_highest(song_name, offset, songlen, segsize, count, output_name, minfre
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res = np.array(res)
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wavfile.write(output_name, sample_rate, res)
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return (loct, sl_t, timing_points) # amplitude result, sliders and frequency result
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def convert_to_wav(song_name:str, output_file="audio.wav") -> str:
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"""
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Converts the song to .wav, only if it's not already in wave format.
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@ -193,11 +264,10 @@ def convert_to_wav(song_name:str, output_file="audio.wav") -> str:
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Returns: the song_name that should be used afterwards.
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"""
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extension = Path(song_name).suffix
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match extension:
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case ".mp3" | ".ogg":
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print("Converting to .wav...")
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subprocess.run(["ffmpeg", "-y", "-i", song_name, output_file], shell=False)
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return output_file
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if(extension == ".mp3" or extension == ".ogg"):
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print("Converting to .wav...")
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subprocess.run(["ffmpeg", "-y", "-i", song_name, output_file], shell=False)
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return output_file
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return song_name
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def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold=0.06, show=True):
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@ -242,20 +312,21 @@ def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold
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plt.show()
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# free()
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'''
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# c-type
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SONG_LEN = 8
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SONG_LEN = 7
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OFFSET = 0.042
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BPM = 149.3
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("songs/ctype.mp3")
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'''
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wavved_song = convert_to_wav("ctype.mp3")
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'''
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# tetris_2
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SONG_LEN = 8
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SONG_LEN = 10
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OFFSET = 0
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BPM = 157
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("tetris_2.wav")
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'''
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'''
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# test
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@ -264,21 +335,21 @@ OFFSET = 0
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BPM = 240
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SEGSIZE = 1/(BPM/60)
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'''
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'''
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# gmtn
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SONG_LEN = 3
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SONG_LEN = 5
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OFFSET = 1.652
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BPM = 155
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("songs/furioso melodia.mp3")
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wavved_song = convert_to_wav("furioso melodia.mp3")
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'''
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'''
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# E
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SONG_LEN = 10
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SONG_LEN = 30
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OFFSET = 2.641
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BPM = 155
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("songs/rushe.mp3")
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wavved_song = convert_to_wav("rushe.mp3")
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'''
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'''
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# Tsubaki
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@ -286,7 +357,7 @@ SONG_LEN = 10
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OFFSET = 35.659
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BPM = 199
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("songs/TSUBAKI.mp3")
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wavved_song = convert_to_wav("TSUBAKI.mp3")
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'''
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'''
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# death
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@ -300,631 +371,7 @@ wavved_song = convert_to_wav("songs/Night of Knights.mp3")
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#wavved_song = convert_to_wav("tetris_2.wav")
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NOTE_DIST = (2**(1/4))
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OCTAVE_DIST = 0.05
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keep_highest(wavved_song, OFFSET, SONG_LEN, SEGSIZE/4, 1, "Zblit.wav", minfreq=300, maxfreq=3000, ampthr=500)
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print("yipee")
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'''
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def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
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# returns a list with peak frequencies alongside the sample rate
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# /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono)
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# segsize is in seconds
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# remove high_pitched/low-pitched frequencies
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minfreq = 110
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maxfreq = 440*6
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# cutting the song to only keep the one we're interested in
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen), "-i", song_name, "crop.wav"], shell=False)
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subprocess.run(["clear"])
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# extracting data from cropped song
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sample_rate, raw_song_data = wavfile.read("crop.wav")
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blit = int(sample_rate*segsize) # Te
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song_data = [0 for i in range(len(raw_song_data))]
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a = 0
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if(is_data_stereo(raw_song_data)):
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print("Converting to mono...")
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for x in range(len(raw_song_data)):
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song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
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if(x % (int(len(raw_song_data)/100)) == 0):
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print(a, "/ 100")
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a += 1
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else:
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song_data = raw_song_data
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print("\nSampleRate : ", sample_rate)
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print("SegSize : ", blit)
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# remove the copy of the song
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subprocess.run(["rm", "crop.wav"], shell=False)
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# calculate the frequencies associated to the FFTs
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pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
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# left boundary of segment to crop
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current_time = offset
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# list of FFTs
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fft_list = []
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# number of samples
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k = 0
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print("Retrieving freqs from", offset, "to", songlen+offset, "...")
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print("amplitudes are from", minfreq, "to", maxfreq)
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while(current_time < songlen+offset-segsize):
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# index corresponding to left boundary
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left_id = int(current_time*sample_rate)
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# index corresponding to right boundary
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right_id = int((current_time+segsize)*sample_rate)
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# calculate the fft, append it to fft_list
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pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
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fft_list.append(pff)
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#print("(k =", k, ") :", left_id, "to", right_id)
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# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
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k += 1
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current_time = offset + k*segsize
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#print(current_time)
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# spacing between samples (time)
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fe = segsize/sample_rate
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# list that will contain the maximum frequencies/amplitudes for all FFTs
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maxlist = []
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maxamps = []
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print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
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# find all maximums
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for i in range(len(fft_list)):
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current_max = -1
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current_fmax = 0
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for j in range(len(fft_list[i])):
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if(j < len(pfreq) and pfreq[j] < maxfreq and pfreq[j] >= minfreq and np.abs(fft_list[i][j]) > current_max):
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current_max = np.abs(fft_list[i][j])
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current_fmax = pfreq[j]
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maxlist.append(current_fmax)
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maxamps.append(current_max)
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# gg
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# maxlist[i] corresponds to time (offset + i*segsize)
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return (maxlist, maxamps)
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def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampthr, output_name):
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# removes unnecessary frequencies/amps from a song
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# ampthr is in [0, 1]
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# extracting data from cropped song
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sample_rate, raw_song_data = wavfile.read(song_name)
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blit = int(sample_rate*segsize) # Te
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song_data = [0 for i in range(len(raw_song_data))]
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a = 0
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if(is_data_stereo(raw_song_data)):
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print("Converting to mono...")
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for x in range(len(raw_song_data)):
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song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
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if(x % (int(len(raw_song_data)/100)) == 0):
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print(a, "/ 100")
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a += 1
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else:
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song_data = raw_song_data
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print("\nSampleRate : ", sample_rate)
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print("SegSize : ", blit)
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# calculate the frequencies associated to the FFTs
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pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
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# left boundary of segment to crop
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current_time = offset
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# list of FFTs
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fft_list = []
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# number of samples
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k = 0
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print("Retrieving freqs from", offset, "to", songlen+offset, "...")
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print("Freqs are from", minfreq, "to", maxfreq)
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while(current_time < songlen+offset-segsize):
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# index corresponding to left boundary
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left_id = int(current_time*sample_rate)
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# index corresponding to right boundary
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right_id = int((current_time+segsize)*sample_rate)
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# calculate the fft, append it to fft_list
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pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
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fft_list.append(pff)
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# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
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k += 1
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current_time = offset + k*segsize
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#print(current_time)
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print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
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# remove
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# i = time, j = freq
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for i in range(len(fft_list)):
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# get the local max freq
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lmax = 0
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for j in range(len(fft_list[i])):
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if(np.abs(fft_list[i][j]) > lmax):
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lmax = np.abs(fft_list[i][j])
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# remove freqs + amps
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for j in range(len(fft_list[i])):
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if(j < len(pfreq)):
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if(pfreq[j] <= minfreq or pfreq[j] >= maxfreq):
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fft_list[i][j] = 0+0j
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if(np.abs(fft_list[i][j]) <= lmax*ampthr):
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fft_list[i][j] = 0+0j
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# writing new .wav
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res = []
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print("Converting...")
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for i in range(len(fft_list)):
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ift = scp.fft.irfft(fft_list[i], n=blit)
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for k in ift:
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res.append(k)
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#print(type(res[0]))
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mx = 0
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for j in range(len(res)):
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if(res[j] > mx):
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mx = res[j]
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for i in range(len(res)):
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res[i] = np.int16(32767*res[i]/mx)
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res = np.array(res)
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wavfile.write(output_name, sample_rate, res)
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def is_within_reach(xref, x, error):
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return(np.abs(xref - x) <= error)
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def localize_frequencies(song_name, offset, songlen, segsize, output_name):
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# removes unnecessary frequencies/amps from a song
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# ampthr is in [0, 1]
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# extracting data from cropped song
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sample_rate, raw_song_data = wavfile.read(song_name)
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blit = int(sample_rate*segsize) # Te
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song_data = [0 for i in range(len(raw_song_data))]
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a = 0
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if(is_data_stereo(raw_song_data)):
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print("Converting to mono...")
|
||||
for x in range(len(raw_song_data)):
|
||||
song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
|
||||
|
||||
if(x % (int(len(raw_song_data)/100)) == 0):
|
||||
print(a, "/ 100")
|
||||
a += 1
|
||||
else:
|
||||
song_data = raw_song_data
|
||||
|
||||
print("\nSampleRate : ", sample_rate)
|
||||
print("SegSize : ", blit)
|
||||
|
||||
# calculate the frequencies associated to the FFTs
|
||||
pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
|
||||
|
||||
# left boundary of segment to crop
|
||||
current_time = offset
|
||||
|
||||
# list of FFTs
|
||||
fft_list = []
|
||||
|
||||
# number of samples
|
||||
k = 0
|
||||
|
||||
print("Retrieving freqs from", offset, "to", songlen+offset, "...")
|
||||
while(current_time < songlen+offset-segsize):
|
||||
# index corresponding to left boundary
|
||||
left_id = int(current_time*sample_rate)
|
||||
|
||||
# index corresponding to right boundary
|
||||
right_id = int((current_time+segsize)*sample_rate)
|
||||
|
||||
# calculate the fft, append it to fft_list
|
||||
pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
|
||||
fft_list.append(pff)
|
||||
|
||||
# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
|
||||
k += 1
|
||||
current_time = offset + k*segsize
|
||||
#print(current_time)
|
||||
|
||||
print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
|
||||
|
||||
let_ring_currentLength = 0
|
||||
let_ring_freq = -200
|
||||
|
||||
let_ring_freqLeniency = 50
|
||||
|
||||
let_ring_leniencySecond = 0.07
|
||||
let_ring_lengthLeniency = 1
|
||||
|
||||
# remove
|
||||
# i = time, j = freq
|
||||
for i in range(len(fft_list)):
|
||||
# retrieve dominant freq
|
||||
maxfreq = 0
|
||||
maxfreqid = 0
|
||||
maxamp = 0
|
||||
for j in range(len(fft_list[i])):
|
||||
if(np.abs(fft_list[i][j]) > maxamp):
|
||||
maxamp = np.abs(fft_list[i][j])
|
||||
maxfreq = pfreq[j]
|
||||
maxfreqid = j
|
||||
|
||||
if(is_within_reach(maxfreq, let_ring_freq, let_ring_freqLeniency)):
|
||||
let_ring_freq = maxfreq
|
||||
let_ring_currentLength += 1
|
||||
|
||||
if(let_ring_currentLength > let_ring_lengthLeniency):
|
||||
for j in range(len(fft_list[i])):
|
||||
#if(j < len(pfreq) and is_within_reach(pfreq[j], maxfreq, 75)):
|
||||
fft_list[i][j] = 0+0j
|
||||
print("here")
|
||||
|
||||
else:
|
||||
let_ring_freq = maxfreq
|
||||
let_ring_currentLength = 0
|
||||
|
||||
print("(at time", i, ") maxfreq and length are", let_ring_freq, let_ring_currentLength)
|
||||
|
||||
# writing new .wav
|
||||
res = []
|
||||
print("Converting...")
|
||||
for i in range(len(fft_list)):
|
||||
ift = scp.fft.irfft(fft_list[i], n=blit)
|
||||
for k in ift:
|
||||
res.append(k)
|
||||
#print(type(res[0]))
|
||||
mx = 0
|
||||
for j in range(len(res)):
|
||||
if(res[j] > mx):
|
||||
mx = res[j]
|
||||
|
||||
for i in range(len(res)):
|
||||
res[i] = np.int16(32767*res[i]/mx)
|
||||
|
||||
res = np.array(res)
|
||||
wavfile.write(output_name, sample_rate, res)
|
||||
|
||||
def write_result(song_name, offset, songlen, segsize, timing_pts, output_name):
|
||||
# removes unnecessary frequencies/amps from a song
|
||||
# ampthr is in [0, 1]
|
||||
|
||||
# extracting data from cropped song
|
||||
sample_rate, raw_song_data = wavfile.read(song_name)
|
||||
blit = int(sample_rate*segsize) # Te
|
||||
|
||||
song_data = [0 for i in range(len(raw_song_data))]
|
||||
|
||||
a = 0
|
||||
if(is_data_stereo(raw_song_data)):
|
||||
print("Converting to mono...")
|
||||
for x in range(len(raw_song_data)):
|
||||
song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
|
||||
|
||||
if(x % (int(len(raw_song_data)/100)) == 0):
|
||||
print(a, "/ 100")
|
||||
a += 1
|
||||
else:
|
||||
song_data = raw_song_data
|
||||
|
||||
print("\nSampleRate : ", sample_rate)
|
||||
print("SegSize : ", blit)
|
||||
|
||||
# calculate the frequencies associated to the FFTs
|
||||
pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
|
||||
|
||||
# left boundary of segment to crop
|
||||
current_time = offset
|
||||
|
||||
# list of FFTs
|
||||
fft_list = []
|
||||
|
||||
# number of samples
|
||||
k = 0
|
||||
|
||||
print("Retrieving freqs from", offset, "to", songlen+offset, "...")
|
||||
while(current_time < songlen+offset-segsize):
|
||||
# index corresponding to left boundary
|
||||
left_id = int(current_time*sample_rate)
|
||||
|
||||
# index corresponding to right boundary
|
||||
right_id = int((current_time+segsize)*sample_rate)
|
||||
|
||||
# calculate the fft, append it to fft_list
|
||||
pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
|
||||
fft_list.append(pff)
|
||||
|
||||
# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
|
||||
k += 1
|
||||
current_time = offset + k*segsize
|
||||
#print(current_time)
|
||||
|
||||
print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
|
||||
|
||||
i0 = 0
|
||||
timing_pts.append(999999)
|
||||
|
||||
write_freq = 880
|
||||
write_cur = 0
|
||||
write_id = -1
|
||||
while(write_cur <= write_freq): # shouldnt seg fault
|
||||
write_id += 1
|
||||
write_cur = pfreq[write_id]
|
||||
|
||||
|
||||
# remove
|
||||
# i = time, j = freq
|
||||
for i in range(len(fft_list)):
|
||||
# retrieve dominant freq
|
||||
if(segsize*i >= timing_pts[i0]):
|
||||
i0 += 1
|
||||
|
||||
maxfreq = 0
|
||||
maxfreqid = 0
|
||||
maxamp = 0
|
||||
for j in range(len(fft_list[i])):
|
||||
if(np.abs(fft_list[i][j]) > maxamp):
|
||||
maxamp = np.abs(fft_list[i][j])
|
||||
maxfreq = pfreq[j]
|
||||
maxfreqid = j
|
||||
|
||||
fft_list[i][write_id] = max(maxamp*2, 32767)
|
||||
fft_list[i][write_id-1] = max(maxamp*2, 32767)
|
||||
fft_list[i][write_id+1] = max(maxamp*2, 32767)
|
||||
|
||||
|
||||
# writing new .wav
|
||||
res = []
|
||||
print("Converting...")
|
||||
for i in range(len(fft_list)):
|
||||
ift = scp.fft.irfft(fft_list[i], n=blit)
|
||||
for k in ift:
|
||||
res.append(k)
|
||||
#print(type(res[0]))
|
||||
mx = 0
|
||||
for j in range(len(res)):
|
||||
if(res[j] > mx):
|
||||
mx = res[j]
|
||||
|
||||
for i in range(len(res)):
|
||||
res[i] = np.int16(32767*res[i]/mx)
|
||||
|
||||
res = np.array(res)
|
||||
wavfile.write(output_name, sample_rate, res)
|
||||
|
||||
def retrieve_dominant_amps(song_name, offset, songlen, segsize, percent, divlen):
|
||||
# returns a list with the percent% peak amplitudes alongside the sample rate
|
||||
# /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono)
|
||||
# segsize is in seconds
|
||||
# divlen is in seconds
|
||||
|
||||
# cutting the song to only keep the one we're interested in
|
||||
subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen), "-i", song_name, "crop.wav"], shell=False)
|
||||
subprocess.run(["clear"])
|
||||
|
||||
# extracting data from cropped song
|
||||
sample_rate, raw_song_data = wavfile.read("crop.wav")
|
||||
blit = int(sample_rate*segsize) # Te
|
||||
|
||||
# in case song has stereo format, conversion to mono
|
||||
song_data = [0 for i in range(len(raw_song_data))]
|
||||
|
||||
a = 0
|
||||
if(is_data_stereo(raw_song_data)):
|
||||
print("Converting to mono...")
|
||||
for x in range(len(raw_song_data)):
|
||||
song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
|
||||
|
||||
if(x % (int(len(raw_song_data)/100)) == 0):
|
||||
print(a, "/ 100")
|
||||
a += 1
|
||||
else:
|
||||
song_data = raw_song_data
|
||||
|
||||
# which notes will be voided
|
||||
is_locked = [False for i in range(len(song_data))]
|
||||
x = int((len(song_data)*percent)//100)
|
||||
|
||||
# length of segments
|
||||
seglen = int(divlen*sample_rate)
|
||||
|
||||
# current offset
|
||||
curptr = 0
|
||||
|
||||
print("Retreiving the", int(x), "/", len(song_data), "highest values")
|
||||
while(curptr < len(song_data)):
|
||||
left = curptr
|
||||
right = min(len(song_data), curptr+seglen)
|
||||
|
||||
#returns a list of couples [id, value]
|
||||
elements = heapq.nlargest(int(x), enumerate(song_data[left:right]), key=lambda x: x[1])
|
||||
|
||||
for idx in range(len(elements)):
|
||||
is_locked[elements[idx][0]+left] = True
|
||||
|
||||
curptr += seglen
|
||||
|
||||
for r in range(len(song_data)):
|
||||
if(is_locked[r] == False):
|
||||
song_data[r] = 0
|
||||
|
||||
# now we need to reduce song_data so that it matches the length of the previous function's return
|
||||
res = []
|
||||
k = 0
|
||||
current_time = offset
|
||||
|
||||
while(current_time < songlen+offset-segsize):
|
||||
# index corresponding to left boundary
|
||||
left_id = int(current_time*sample_rate)
|
||||
|
||||
# index corresponding to right boundary
|
||||
right_id = int((current_time+segsize)*sample_rate)
|
||||
|
||||
# merge the segment into one value
|
||||
cmax = 0
|
||||
for i in range(left_id, right_id):
|
||||
if(i < len(song_data) and cmax < song_data[i]):
|
||||
cmax = song_data[i]
|
||||
|
||||
res.append(cmax)
|
||||
|
||||
k += 1
|
||||
current_time = offset + k*segsize
|
||||
|
||||
# gg
|
||||
# res[i] corresponds to time (offset + i*segsize)
|
||||
return res
|
||||
|
||||
def parse_after_filter(song_name, offset, songlen, segsize, dt0):
|
||||
sample_rate, raw_song_data = wavfile.read(song_name)
|
||||
blit = int(sample_rate*segsize) # Te
|
||||
|
||||
song_data = [0 for i in range(len(raw_song_data))]
|
||||
|
||||
a = 0
|
||||
if(is_data_stereo(raw_song_data)):
|
||||
print("Converting to mono...")
|
||||
for x in range(len(raw_song_data)):
|
||||
song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
|
||||
|
||||
if(x % (int(len(raw_song_data)/100)) == 0):
|
||||
print(a, "/ 100")
|
||||
a += 1
|
||||
else:
|
||||
song_data = raw_song_data
|
||||
|
||||
print("\nSampleRate : ", sample_rate)
|
||||
print("SegSize : ", blit)
|
||||
|
||||
# calculate the frequencies associated to the FFTs
|
||||
pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
|
||||
|
||||
# left boundary of segment to crop
|
||||
current_time = offset
|
||||
|
||||
# list of FFTs
|
||||
fft_list = []
|
||||
|
||||
# number of samples
|
||||
k = 0
|
||||
|
||||
print("Retrieving freqs from", offset, "to", songlen+offset, "...")
|
||||
while(current_time < songlen+offset-segsize):
|
||||
# index corresponding to left boundary
|
||||
left_id = int(current_time*sample_rate)
|
||||
|
||||
# index corresponding to right boundary
|
||||
right_id = int((current_time+segsize)*sample_rate)
|
||||
|
||||
# calculate the fft, append it to fft_list
|
||||
pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
|
||||
fft_list.append(pff)
|
||||
|
||||
# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
|
||||
k += 1
|
||||
current_time = offset + k*segsize
|
||||
#print(current_time)
|
||||
|
||||
print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
|
||||
|
||||
timing_points = []
|
||||
prev_append = 1
|
||||
|
||||
for i in range(len(fft_list)):
|
||||
# retrieve dominant freq
|
||||
maxfreq = 0
|
||||
maxfreqid = 0
|
||||
maxamp = 0
|
||||
for j in range(len(fft_list[i])):
|
||||
if(np.abs(fft_list[i][j]) > maxamp):
|
||||
maxamp = np.abs(fft_list[i][j])
|
||||
maxfreq = pfreq[j]
|
||||
maxfreqid = j
|
||||
|
||||
print("dominant at", i, ":" , maxamp)
|
||||
if(maxamp > 100):
|
||||
if(prev_append > 0):
|
||||
timing_points.append(i*segsize + dt0)
|
||||
prev_append -= 1
|
||||
else:
|
||||
prev_append = 2
|
||||
|
||||
print(timing_points)
|
||||
return timing_points
|
||||
|
||||
# remove high/low frequencies (often noise)
|
||||
#void_freq_clean(wavved_song, OFFSET, SONG_LEN, SEGSIZE/8, 100, 3000, 0.05, "Zvoided_song.wav")
|
||||
|
||||
# crops any part with let ring
|
||||
localize_frequencies("Zblit.wav", 0, SONG_LEN-0.1, SEGSIZE/8, "Zcleaned_song.wav")
|
||||
#localize_frequencies(wavved_song, OFFSET, SONG_LEN, SEGSIZE/8, "Zcleaned_song.wav")
|
||||
|
||||
# find timings
|
||||
tp = parse_after_filter("Zcleaned_song.wav", 0, SONG_LEN-0.1, SEGSIZE/8, OFFSET)
|
||||
|
||||
# write
|
||||
write_result(wavved_song, OFFSET, SONG_LEN-0.1, SEGSIZE/8, tp, "Zoutput_song.wav")
|
||||
|
||||
#retrieve_all_from_song("Zcleaned_song.wav", 0, 5, 149.3, dtf=1/(149.3/60)/8)
|
||||
'''
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue