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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# CREATED:2015-02-16 13:10:05 by Brian McFee <brian.mcfee@nyu.edu>
"""Regression tests on metlab features"""
# Disable cache
import os
import pytest
try:
os.environ.pop("LIBROSA_CACHE_DIR")
except KeyError:
pass
import numpy as np
import scipy.io
import scipy.signal
from test_core import load, files
import librosa
__EXAMPLE_FILE = os.path.join("tests", "data", "test1_22050.wav")
def met_stft(y, n_fft, hop_length, win_length, normalize):
S = np.abs(
librosa.stft(
y,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=scipy.signal.windows.hamming,
center=False,
)
)
if normalize:
S = S / (S[0] + np.sum(2 * S[1:], axis=0))
return S
@pytest.mark.parametrize(
"infile", files(os.path.join("tests", "data", "met-centroid-*.mat"))
)
def test_spectral_centroid(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]), sr=None, mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
# spectralCentroid uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
centroid = librosa.feature.spectral_centroid(
S=S, sr=sr, n_fft=n_fft, hop_length=hop_length
)
assert np.allclose(centroid, DATA["centroid"])
@pytest.mark.parametrize(
"infile", files(os.path.join("tests", "data", "met-contrast-*.mat"))
)
def test_spectral_contrast(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]), sr=None, mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
# spectralContrast uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
contrast = librosa.feature.spectral_contrast(
S=S, sr=sr, n_fft=n_fft, hop_length=hop_length, linear=True
)
assert np.allclose(contrast, DATA["contrast"], rtol=1e-3, atol=1e-2)
@pytest.mark.parametrize(
"infile", files(os.path.join("tests", "data", "met-rolloff-*.mat"))
)
def test_spectral_rolloff(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]), sr=None, mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
pct = DATA["pct"][0, 0]
# spectralRolloff uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
rolloff = librosa.feature.spectral_rolloff(
S=S, sr=sr, n_fft=n_fft, hop_length=hop_length, roll_percent=pct
)
assert np.allclose(rolloff, DATA["rolloff"])
@pytest.mark.parametrize(
"infile", files(os.path.join("tests", "data", "met-bandwidth-*.mat"))
)
def test_spectral_bandwidth(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]), sr=None, mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
S = DATA["S"]
# normalization is disabled here, since the precomputed S is already
# normalized
# metlab uses p=1, other folks use p=2
bw = librosa.feature.spectral_bandwidth(
S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
centroid=DATA["centroid"],
norm=False,
p=1,
)
# METlab implementation takes the mean, not the sum
assert np.allclose(bw, S.shape[0] * DATA["bw"])
|
