我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用librosa.frames_to_time()。
def get_mfcc(x, sr, n_mfcc=20): """Compute MFCC features from raw audio, using librosa. Librosa must be installed. Args: x (1d-array) audio signal, mono sr (int): sample rate n_mfcc (int): number of coefficients to retain Returns: 2d-array: MFCC features """ mfcc_all = librosa.feature.mfcc(x, sr) n_coeff, n_frames = mfcc_all.shape t = librosa.frames_to_time(np.arange(n_frames), sr=sr, hop_length=512) return t, mfcc_all[:n_mfcc].T
def get_beats(x, sr): """Track beats in an audio excerpt, using librosa's standard beat tracker. Args: x (1d-array) audio signal, mono sr (int): sample rate Returns: 2d-array: beat times and beat intervals """ _, beat_frames = librosa.beat.beat_track(x, sr=sr) beat_times = librosa.frames_to_time(beat_frames, sr=sr) t = beat_times[:-1,] beat_intervals = np.diff(beat_times) return t, beat_intervals
def get_onsets(x, sr): """Compute inter-onset intervals (IOI) from audio, using librosa. Args: x (1d-array) audio signal, mono sr (int): sample rate Returns: 2d-array: onset times and IOI """ onset_frames = librosa.onset.onset_detect(x, sr=sr) onset_times = librosa.frames_to_time(onset_frames, sr=sr) t = onset_times[:-1,] onset_intervals = np.diff(onset_times) return t, onset_intervals
def decode_events(self, encoded): '''Decode labeled events into (time, value) pairs Parameters ---------- encoded : np.ndarray, shape=(n_frames, m) Frame-level annotation encodings as produced by ``encode_events``. Real-valued inputs are thresholded at 0.5. Returns ------- [(time, value)] : iterable of tuples where `time` is the event time and `value` is an np.ndarray, shape=(m,) of the encoded value at that time ''' if np.isrealobj(encoded): encoded = (encoded >= 0.5) times = frames_to_time(np.arange(encoded.shape[0]), sr=self.sr, hop_length=self.hop_length) return zip(times, encoded)
def _crossFadeRegion(self): # Computes the cross fade region for the mixed song Na = self.beats['in'].shape[0]-1 scores = [self._score(i, Na) for i in xrange(2, int(Na/4))] noBeats = np.argmax(scores)+2 inDuration = librosa.get_duration(y=self.Yin, sr=self.sr) fadeInStart = librosa.frames_to_time(self.beats['in'], sr=self.sr)[-int(noBeats/2)] fadeIn = inDuration - fadeInStart fadeOut = librosa.frames_to_time(self.beats['out'], sr=self.sr)[int(noBeats/2)] print "Best Power Corelation Scores=", np.max(scores) print "Number of beats in cross fade region=", noBeats print "fadeInStart=", fadeInStart print "fadeOutEnd=", fadeOut print "Cross Fade Time=", fadeIn+fadeOut self.crossFade = [fadeInStart*1000, fadeOut*1000] # In milliseconds
def calculateFeatures(filename): y, sr = librosa.load(filename) hop_length = 512 section_length = 30 permit_length = 0 #No overlap n_paras = 4 tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr) n_beats = len(beat_frames) y_harmonic, _ = librosa.effects.hpss(y) beat_times = librosa.frames_to_time(beat_frames, sr=sr) mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=1) mfcc_delta = librosa.feature.delta(mfcc) beat_mfcc_delta = librosa.util.sync(np.vstack([mfcc, mfcc_delta]), beat_frames) chromagram = librosa.feature.chroma_cqt(y=y_harmonic, sr=sr) beat_chroma = librosa.util.sync(chromagram, beat_frames, aggregate=np.median) delta_rms = np.square(beat_mfcc_delta[1]) prev_delta_sums = np.empty((n_beats-section_length+1)) total_sum = np.sum(delta_rms[:section_length]) prev_delta_sums[0] = total_sum for pos in range(n_beats-section_length): total_sum = total_sum - delta_rms[pos] + delta_rms[pos+section_length] prev_delta_sums[pos+1] = total_sum prev_delta_sums_delta = librosa.feature.delta(prev_delta_sums) para_init_locs = [] for n_p in range(n_paras): lowest = 50 lowest_loc = 0 for loc, each_sum_delta in enumerate(prev_delta_sums_delta): #Check valid loc valid_loc = True for each_loc in para_init_locs: if loc > each_loc - (section_length - permit_length) and loc < each_loc + (section_length - permit_length): valid_loc = False break if each_sum_delta <= lowest and valid_loc: lowest = each_sum_delta lowest_loc = loc para_init_locs.append(lowest_loc) para_init_locs.sort() #print(para_init_locs) all_features = np.empty((n_paras*section_length, 2)) #0 - mfcc, 1 - chroma for n_p in range(n_paras): all_features[n_p*section_length:(n_p+1)*section_length, 0] = beat_mfcc_delta[0][para_init_locs[n_p]:para_init_locs[n_p]+section_length] / 250 all_features[n_p*section_length:(n_p+1)*section_length, 1] = np.argmax(beat_chroma[:, para_init_locs[n_p]:para_init_locs[n_p]+section_length], axis=0)/11 return all_features.reshape((n_paras*section_length*2))
