| transform_mel_spectrogram | R Documentation | 
Create MelSpectrogram for a raw audio signal. This is a composition of Spectrogram and MelScale.
transform_mel_spectrogram( sample_rate = 16000, n_fft = 400, win_length = NULL, hop_length = NULL, f_min = 0, f_max = NULL, pad = 0, n_mels = 128, window_fn = torch::torch_hann_window, power = 2, normalized = FALSE, ... )
| sample_rate | (int, optional): Sample rate of audio signal. (Default:  | 
| n_fft | (int, optional): Size of FFT, creates  | 
| win_length | (int or NULL, optional): Window size. (Default:  | 
| hop_length | (int or NULL, optional): Length of hop between STFT windows. (Default:  | 
| f_min | (float, optional): Minimum frequency. (Default:  | 
| f_max | (float or NULL, optional): Maximum frequency. (Default:  | 
| pad | (int, optional): Two sided padding of signal. (Default:  | 
| n_mels | (int, optional): Number of mel filterbanks. (Default:  | 
| window_fn | (function, optional): A function to create a window tensor
that is applied/multiplied to each frame/window. (Default:  | 
| power | (float, optional): Power of the norm. (Default: to  | 
| normalized | (logical): Whether to normalize by magnitude after stft (Default:  | 
| ... | (optional): Arguments for window function. | 
forward param: waveform (Tensor): Tensor of audio of dimension (..., time).
tensor: Mel frequency spectrogram of size (..., n_mels, time).
https://timsainb.github.io/spectrograms-mfccs-and-inversion-in-python.html
https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
#'   Example
## Not run: 
if(torch::torch_is_installed()) {
mp3_path <- system.file("sample_audio_1.mp3", package = "torchaudio")
sample_mp3 <- transform_to_tensor(tuneR_loader(mp3_path))
# (channel, n_mels, time)
mel_specgram <- transform_mel_spectrogram(sample_rate = sample_mp3[[2]])(sample_mp3[[1]])
}
## End(Not run)
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