A modification of the spectrogram (log magnitude of the short-time Fourier transform) to more accurately show the instantaneous frequencies of signal components was first proposed in 1976 [Kodera et al., Phys. Earth Planet. Inter. 12, 142-150 (1976)], and has been considered or reinvented a few times since but never widely adopted. This paper presents a unified theoretical picture of this time-...

In this study, we propose a deep neural network for reconstructing intelligible speech from silent lip movement videos. We use auditory spectrogram as spectral representation of speech and its corresponding sound generation method resulting in a more natural sounding reconstructed speech. Our proposed network consists of an autoencoder to extract bottleneck features from the auditory spectrogra...

Marine mammal vocalizations are often analyzed using time-frequency representations (TFRs) which highlight their nonstationarities. One commonly used TFR is the spectrogram. The characteristic spectrogram time-frequency (TF) contours of marine mammal vocalizations play a significant role in whistle classification and individual or group identification. A major hurdle in the robust automated ext...

For the past five decades there has been a substantial increase in the development of computer applications based on the STFT (Short-Time Fourier Transform) spectrograms. Amongst others, a relevant example of this kind of implementation is speech analysis. Indeed, if the audio signal is correctly filtered, it is possible to use STFT spectrogram techniques to recognize speech. This work investig...

This paper presents a method of separating speech and interference signal from their single mixture. The system is based on deriving some independent basis vectors from the mixture spectrogram and clustering them to produce the individual source subspaces. Principal component analysis (PCA) is used to derive some basis vectors reducing the dimension of the mixture spectrogram and independent co...

Radio Frequency Interference (RFI) detection and mitigation algorithms based on a signal’s spectrogram (frequency and time domain representation) are presented. The radiometric signal’s spectrogram is treated as an image, and therefore image processing techniques are applied to detect and mitigate RFI by two-dimensional filtering. A series of Monte-Carlo simulations have been performed to evalu...

We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram mask...

In this paper, we introduce an audio mosaicing technique based on performing posterior inference on a probabilistic generative model. Whereas previous approaches to concatenative synthesis and audio mosaicing have mostly tried to match higher-level descriptors of audio or individual STFT frames, we try to directly match the magnitude spectrogram of a target sound by combining and overlapping a ...