我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用librosa.cqt()。
def get_seeds(self, audio_filepath): """Get the seeds file to pass to the HLL tracker. Parameters ---------- audio_filepath : str Path to audio file. Returns ------- seeds_fpath : str Path to the seeds output file. """ y, sr = librosa.load(audio_filepath, sr=44100) y_harmonic = librosa.effects.harmonic(y) cqt, samples, freqs = self._compute_cqt(y_harmonic, sr) seeds = self._pick_seeds_cqt(cqt, freqs, samples) seeds_fpath = tmp.mktemp('.csv') with open(seeds_fpath, 'w') as fhandle: writer = csv.writer(fhandle, delimiter=',') writer.writerows(seeds) return seeds_fpath
def _pick_seeds_cqt(self, cqt, cqt_freqs, samples): """Compute a CQT. Parameters ---------- cqt : np.array [n_samples, n_freqs] Log amplitude CQT. freqs : np.array [n_freqs] CQT frequencies. samples : np.array [n_samples] CQT time stamps. Returns ------- seeds : np.array [n_seeds, 2] Array of time, frequency seeds """ seeds = [] for i, freq in enumerate(cqt_freqs): freq_band = cqt[i, :] freq_band_smooth = self._moving_average(freq_band) peak_locs = librosa.util.peak_pick( freq_band_smooth, self.pre_max, self.post_max, self.pre_avg, self.post_avg, self.delta, self.wait ) if len(peak_locs) > 0: peak_locs = peak_locs[ (freq_band[peak_locs] > self.peak_thresh) ] for peak_loc in peak_locs: sample = samples[peak_loc] seeds.append([sample, freq]) seeds = np.array(seeds) return seeds
def transform_audio(self, y): '''Compute the CQT Parameters ---------- y : np.ndarray The audio buffer Returns ------- data : dict data['mag'] : np.ndarray, shape = (n_frames, n_bins) The CQT magnitude data['phase']: np.ndarray, shape = mag.shape The CQT phase ''' n_frames = self.n_frames(get_duration(y=y, sr=self.sr)) C = cqt(y=y, sr=self.sr, hop_length=self.hop_length, fmin=self.fmin, n_bins=(self.n_octaves * self.over_sample * 12), bins_per_octave=(self.over_sample * 12)) C = fix_length(C, n_frames) cqtm, phase = magphase(C) if self.log: cqtm = amplitude_to_db(cqtm, ref=np.max) return {'mag': cqtm.T.astype(np.float32)[self.idx], 'phase': np.angle(phase).T.astype(np.float32)[self.idx]}
def transform_audio(self, y): '''Compute the HCQT Parameters ---------- y : np.ndarray The audio buffer Returns ------- data : dict data['mag'] : np.ndarray, shape = (n_frames, n_bins, n_harmonics) The CQT magnitude data['phase']: np.ndarray, shape = mag.shape The CQT phase ''' cqtm, phase = [], [] n_frames = self.n_frames(get_duration(y=y, sr=self.sr)) for h in self.harmonics: C = cqt(y=y, sr=self.sr, hop_length=self.hop_length, fmin=self.fmin * h, n_bins=(self.n_octaves * self.over_sample * 12), bins_per_octave=(self.over_sample * 12)) C = fix_length(C, n_frames) C, P = magphase(C) if self.log: C = amplitude_to_db(C, ref=np.max) cqtm.append(C) phase.append(P) cqtm = np.asarray(cqtm).astype(np.float32) phase = np.angle(np.asarray(phase)).astype(np.float32) return {'mag': self._index(cqtm), 'phase': self._index(phase)}
def compute_spec(audio_file,spectro_file): # Get actual audio audio, sr = librosa.load(audio_file, sr=config['resample_sr']) # Compute spectrogram if config['spectrogram_type']=='cqt': spec = librosa.cqt(audio, sr=sr, hop_length=config['hop'], n_bins=config['cqt_bins'], real=False) elif config['spectrogram_type']=='mel': spec = librosa.feature.melspectrogram(y=audio, sr=sr, hop_length=config['hop'],n_fft=config['n_fft'],n_mels=config['n_mels']) elif config['spectrogram_type']=='stft': spec = librosa.stft(y=audio,n_fft=config['n_fft']) # Write results: with open(spectro_file, "w") as f: pickle.dump(spec, f, protocol=-1) # spec shape: MxN.
def rcqt(PS, iters=500, sr=22050, hop_length=512, n_bins=84, bins_per_octave=12): sig_len = (PS.shape[1]-1)*hop_length p = 2 * np.pi * np.random.random_sample(PS.shape) - np.pi for i in range(iters): print i S = PS * np.exp(1j*p) X = icqt(S, sr=sr, hop_length=hop_length, bins_per_octave=bins_per_octave) X = librosa.util.fix_length(X, sig_len) p = np.angle(cqt(X, sr=sr, hop_length=hop_length, n_bins=n_bins, bins_per_octave=bins_per_octave)) return X
def feature_extract(songfile_name): ''' takes: filename outputs: audio feature representation from that file (currently cqt) **assumes working directory contains raw song files** returns a tuple containing songfile name and numpy array of song features ''' song_loc = os.path.abspath(songfile_name) y, sr = librosa.load(song_loc) desire_spect_len = 2580 C = librosa.cqt(y=y, sr=sr, hop_length=512, fmin=None, n_bins=84, bins_per_octave=12, tuning=None, filter_scale=1, norm=1, sparsity=0.01, real=False) # get log-power spectrogram with noise floor of -80dB C = librosa.logamplitude(C**2, ref_power=np.max) # scale log-power spectrogram to positive integer value for smaller footpint noise_floor_db = 80 scaling_factor = (2**16 - 1)/noise_floor_db C += noise_floor_db C *= scaling_factor C = C.astype('uint16') # if spectral respresentation too long, crop it, otherwise, zero-pad if C.shape[1] >= desire_spect_len: C = C[:,0:desire_spect_len] else: C = np.pad(C,((0,0),(0,desire_spect_len-C.shape[1])), 'constant') return songfile_name, C
def create_feature_matrix_spark(song_files): # cqt wrapper def log_cqt(y,sr): C = librosa.cqt(y=y, sr=sr, hop_length=512, fmin=None, n_bins=84, bins_per_octave=12, tuning=None, filter_scale=1, norm=1, sparsity=0.01, real=True) # get log-power spectrogram with noise floor of -80dB C = librosa.logamplitude(C**2, ref_power=np.max) # scale log-power spectrogram to positive integer value for smaller footpint noise_floor_db = 80 scaling_factor = (2**16 - 1)/noise_floor_db C += noise_floor_db C *= scaling_factor C = C.astype('uint16') return C # padding wrapper def padding(C,desired_spect_len): if C.shape[1] >= desired_spect_len: C = C[:,0:desired_spect_len] else: C = np.pad(C,((0,0),(0,desired_spect_len-C.shape[1])), 'constant') return C # load try-catch wrapper def try_load(filename): try: sys.stdout.write('Processing: %s \r' % os.path.basename(filename)) sys.stdout.flush() return librosa.load(filename) except: pass # transormations filesRDD = sc.parallelize(song_files) rawAudioRDD = filesRDD.map(lambda x: (os.path.basename(x),try_load(x))).filter(lambda x: x[1] != None) rawCQT = rawAudioRDD.map(lambda x: (x[int(0)], log_cqt(x[int(1)][int(0)],x[int(1)][int(1)]))) paddedCQT = rawCQT.map(lambda x: (x[0],padding(x[1],2580))) return paddedCQT.collect()
def _compute_cqt(self, y, sr): """Compute a CQT. Parameters ---------- y : np.array Audio signal sr : float Audio singal sample rate Returns ------- cqt_log : np.array [n_samples, n_freqs] Log amplitude CQT. samples : np.array [n_samples] CQT time stamps. freqs : np.array [n_freqs] CQT frequencies. """ fmin = librosa.note_to_hz(self.min_note) bins_per_octave = 12 n_cqt_bins = bins_per_octave * self.n_octaves cqt = np.abs(librosa.cqt( y, sr=sr, hop_length=self.hop_size, fmin=fmin, filter_scale=self.filter_scale, bins_per_octave=bins_per_octave, n_bins=n_cqt_bins )) cqt = self._norm_matrix(cqt) n_time_frames = cqt.shape[1] freqs = librosa.cqt_frequencies( fmin=fmin, bins_per_octave=bins_per_octave, n_bins=n_cqt_bins ) samples = librosa.frames_to_samples( range(n_time_frames), hop_length=self.hop_size ) return cqt, samples, freqs
