mindspore.dataset.audio.SpectralCentroid
- class mindspore.dataset.audio.SpectralCentroid(sample_rate, n_fft=400, win_length=None, hop_length=None, pad=0, window=WindowType.HANN)[source]
Compute the spectral centroid for each channel along the time axis.
- Parameters
sample_rate (int) – Sampling rate of audio signal, e.g.
44100
(Hz).n_fft (int, optional) – Size of FFT, creates n_fft // 2 + 1 bins. Default:
400
.win_length (int, optional) – Window size. Default:
None
, will use n_fft .hop_length (int, optional) – Length of hop between STFT windows. Default:
None
, will use win_length // 2 .pad (int, optional) – Two sided padding of signal. Default:
0
.window (WindowType, optional) – Window function that is applied/multiplied to each frame/window, can be
WindowType.BARTLETT
,WindowType.BLACKMAN
,WindowType.HAMMING
,WindowType.HANN
orWindowType.KAISER
. Default:WindowType.HANN
.
- Raises
TypeError – If sample_rate is not of type int.
ValueError – If sample_rate is a negative number.
TypeError – If n_fft is not of type int.
ValueError – If n_fft is not a positive number.
TypeError – If win_length is not of type int.
ValueError – If win_length is not a positive number.
ValueError – If win_length is greater than n_fft .
TypeError – If hop_length is not of type int.
ValueError – If hop_length is not a positive number.
TypeError – If pad is not of type int.
ValueError – If pad is a negative number.
TypeError – If window is not of type
mindspore.dataset.audio.WindowType
.RuntimeError – If input tensor is not in shape of <…, time>.
- Supported Platforms:
CPU
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.audio as audio >>> >>> # Use the transform in dataset pipeline mode >>> waveform = np.random.random([5, 10, 20]) # 5 samples >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.SpectralCentroid(44100)] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["audio"].shape, item["audio"].dtype) ... break (10, 1, 1) float64 >>> >>> # Use the transform in eager mode >>> waveform = np.random.random([10, 20]) # 1 sample >>> output = audio.SpectralCentroid(44100)(waveform) >>> print(output.shape, output.dtype) (10, 1, 1) float64
- Tutorial Examples: