librosa

I generated spectrogram of a "seven" utterance using the "egs/tidigits" code from Kaldi, using 23 bins, 20kHz sampling rate, 25ms window, and 10ms shift. Spectrogram appears as below visualized via MATLAB imagesc function: I am experimenting with using Librosa as an alternative to Kaldi. I set up my code as below using the same number of bins, sampling rate, and window length / shift as above. time_series, sample_rate = librosa.core.load("7a.wav",sr=20000) spectrogram = librosa.feature.melspectrogram(time_series, sr=20000, n_mels=23, n_fft=500, hop_length=200) log_S = librosa.core.logamplitude

So here's the idea: you can generate a spectrogram from an audio file using shorttime Fourier transform (stft). Then some people have generated something called a "binary mask" to generate different audio (ie. with background noise removed etc.) from the inverse stft. Here's what I understand: stft is a simple equation that is applied to the audio file, which generates the information that can easily be displayed a spectrogram. By taking the inverse of the stft matrix, and multiplying it by a matrix of the same size (the binary matrix) you can create a new matrix with information to generate

I have over 1000 audio files (it's just a initial development, in the future, there will be even more audio files), and would like to convert them to melspectrogram. Since my workstation has a Intel® Xeon® Processor E5-2698 v3, which has 32 threads, I would like to use multithread to do my job. My code import os import librosa from librosa.display import specshow from natsort import natsorted import numpy as np import sys # Libraries for multi thread from multiprocessing.dummy import Pool as ThreadPool import subprocess pool = ThreadPool(20) songlist = os.listdir('../opensmile/devset_2015/')

I am using this algorithm to detect the pitch of this audio file. As you can hear, it is an E2 note played on a guitar with a bit of noise in the background. I generated this spectrogram using STFT: And I am using the algorithm linked above like this: y, sr = librosa.load(filename, sr=40000) pitches, magnitudes = librosa.core.piptrack(y=y, sr=sr, fmin=75, fmax=1600) np.set_printoptions(threshold=np.nan) print pitches[np.nonzero(pitches)] As a result, I am getting pretty much every possible frequency between my fmin and fmax . What do I have to do with the output of the piptrack method to