Multichannel microphone-based reverberation time estimation method and device which use deep neural network
AssigneeIndustry-University Cooperation Foundation Hanyang University
InventorsJoon-Hyuk Chang, Myung In Lee
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Abstract
A multichannel microphone-based reverberation time estimation method and device which use a deep neural network (DNN) are disclosed. A multichannel microphone-based reverberation time estimation method using a DNN, according to one embodiment, comprises the steps of: receiving a voice signal through a multichannel microphone; deriving a feature vector including spatial information by using the inputted voice signal; and estimating the degree of reverberation by applying the feature vector to the DNN.
Claims9
A method for multichannel microphone-based reverberation time estimation using a deep neural network (DNN), the method comprising:
- receiving an input of a voice signal through a multichannel microphone;
- deriving a feature vector that includes spatial information using the input voice signal, and
- estimating a degree of reverberation by applying the feature vector to the DNN,
- wherein the deriving of the feature vector comprises:
- deriving a negative-side variance (NSV) by deriving time and frequency information from the input voice signal using a short-time Fourier transform (STFT) and by deriving distribution of envelopes for each frequency band based on the derived time and frequency information;
- and
- deriving a cross-correlation function representing a correlation between two microphones in the input voice signal, and
- wherein the estimating of the degree of reverberation comprises estimating a reverberation time by using the derived NSV and the cross-correlation function as an input of the DNN.
An apparatus for multichannel microphone-based reverberation time estimation using a deep neural network (DNN), the apparatus comprising:
- an inputter configured to receive an input of a voice signal through a multichannel microphone;
- a feature vector extractor configured to derive a feature vector that includes spatial information using the input voice signal;
- and
- a reverberation estimator configured to estimate a degree of reverberation by applying the feature vector to the DNN,
- wherein the feature vector extractor comprises:
- a negative-side variance (NSV) deriver configured to derive an NSV by deriving time and frequency information from the input voice signal using a short-time Fourier transform (STFT) and by deriving distribution of envelopes for each frequency band based on the derived time and frequency information;
- and
- a cross-correlation function deriver configured to derive a cross-correlation function representing a correlation between two microphones in the input voice signal, and
- wherein the reverberation estimator is configured to estimate a reverberation time by using the derived NSV the cross-correlation function as an input of the DNN.
Dependencies are inferred from the claim text.