Open vs enclosed spatial environment classification for a mobile or wearable device using microphone and deep learning method

1. A method, comprising:

detecting, by one or more microphones, ambient sound in a spatial environment;

outputting, to a processor, an audio signal representative of the ambient sound;

determining, by the processor, a spatial environment impulse response (SEIR) for the audio signal;

extracting one or more features of the SEIR; and

classifying, by a pattern classifier executed by the processor, the spatial environment as open or enclosed based on the one or more features of the SEIR.

2. The method of claim 1 , wherein:

determining the SEIR for the audio signal includes:

performing a deconvolution on the audio signal; and

determining a cepstrum for the deconvoluted audio signal;

the method comprises:

augmenting the one or more features of the SEIR with features extracted from Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs or double delta MFCC to form a composite vector; and

classifying the spatial environment as open or enclosed includes:

classifying the spatial environment as open or enclosed based on the composite vector.

3. The method of claim 1 , wherein classifying the spatial environment as open or enclosed includes identifying a type of the spatial environment as office, home, mall, supermarket, street, stadium, beach or nature.

4. The method of claim 1 , wherein determining the SEIR for the audio signal includes:

dividing the audio signal into a plurality of frames;

determining an energy ratio for each frame of the plurality of frames;

selecting, from the plurality of frames, a set of frames having respective energy ratios that meet a criterion;

performing exponential windowing on the set of frames to minimize phase;

determining a cepstrum for the set of frames; and

performing inverse exponential windowing on the set of frames.

5. The method of claim 1 , wherein extracting the one or more features of the SEIR includes:

obtaining a first SEIR feature of the one or more features as energy of multiple bands of initial samples of the SEIR; and

obtaining a second SEIR feature of the one or more features as an average of maxima indices of SEIR magnitude.

6. The method of claim 5 , wherein extracting the one or more features of the SEIR includes:

obtaining a third SEIR feature of the one or more features as a time kurtosis of the SEIR;

obtaining a fourth SEIR feature of the one or more features as a spectral standard deviation at a center frequency of the SEIR; and

obtaining a fifth SEIR feature of the one or more features as a slope of samples of the SEIR.

7. The method of claim 2 , comprising:

performing cepstral mean subtraction on the features extracted from the MFCC, delta MFCC or double delta MFCC to reduce mismatch between training and testing conditions.

8. A device, comprising:

one or more microphones configured to:

detect ambient sound in a spatial environment; and

output an audio signal representative of the ambient sound; and

a processor configured to:

receive the audio signal representative of the ambient sound;

determine a spatial environment impulse response (SEIR) for the audio signal;

extract one or more features of the SEIR; and

classify the spatial environment as open or enclosed based on the one or more features of the SEIR.

9. The device of claim 8 , wherein the processor is configured to:

determine the SEIR for the audio signal by at least:

performing a deconvolution on the audio signal; and

determining a cepstrum for the deconvoluted audio signal;

augment the one or more features of the SEIR with features extracted from Mel-frequency cepstral coefficients (MFCCs), delta MFCCs or double delta MFCC to form a composite vector; and

classify the spatial environment as open or enclosed by classifying the spatial environment as open or enclosed based on the composite vector.

10. The device of claim 8 , wherein classifying the spatial environment as open or enclosed includes identifying a type of the spatial environment as office, home, mall, supermarket, street, stadium, beach or nature.

11. The device of claim 8 , wherein the processor is configured to determine the SEIR for the audio signal by:

dividing the audio signal into a plurality of frames;

determining an energy ratio for each frame of the plurality of frames;

selecting, from the plurality of frames, a set of frames having respective energy ratios that meet a criterion;

performing exponential windowing on the set of frames to minimize phase;

determining a cepstrum for the set of frames; and

performing inverse exponential windowing on the set of frames.

12. The device of claim 8 , wherein the processor is configured to extract the one or more features of the SEIR by:

obtaining a first SEIR feature of the one or more features as energy of multiple bands of initial samples of the SEIR; and

obtaining a second SEIR feature of the one or more features as an average of maxima indices of SEIR magnitude.

13. The device of claim 12 , wherein the processor is configured to extract the one or more features of the SEIR by:

obtaining a third SEIR feature of the one or more features as a time kurtosis of the SEIR;

obtaining a fourth SEIR feature of the one or more features as a spectral standard deviation at a center frequency of the SEIR; and

obtaining a fifth SEIR feature of the one or more features as a slope of samples of the SEIR.

14. The device of claim 9 , wherein the processor is configured to:

perform cepstral mean subtraction on the features extracted from the MFCC, delta MFCC or double delta MFCC to reduce mismatch between training and testing conditions.

15. A system, comprising:

a processor; and

memory configured to store executable instructions that, when executed by the processor, cause the processor to:

receive an audio signal representative of ambient sound of a spatial environment;

determine a spatial environment impulse response (SEIR) for the audio signal;

extract one or more features of the SEIR; and

classify the spatial environment as open or enclosed based on the one or more features of the SEIR.

16. The system of claim 15 , wherein the executable instructions cause the processor to:

determine the SEIR for the audio signal by at least:

performing a deconvolution on the audio signal; and

determining a cepstrum for the deconvoluted audio signal;

augment the one or more features of the SEIR with features extracted from Mel-frequency cepstral coefficients (MFCCs), delta MFCCs or double delta MFCC to form a composite vector; and

classify the spatial environment as open or enclosed by at least:

classifying the spatial environment as open or enclosed based on the composite vector.

17. The system of claim 15 , wherein classifying the spatial environment as open or enclosed includes identifying a type of the spatial environment as office, home, mall, supermarket, street, stadium, beach or nature.

18. The system of claim 15 , wherein the executable instructions cause the processor to classify the spatial environment as open or enclosed by at least:

dividing the audio signal into a plurality of frames;

determining an energy ratio for each frame of the plurality of frames;

selecting, from the plurality of frames, a set of frames having respective energy ratios that meet a criterion;

performing exponential windowing on the set of frames to minimize phase;

determining a cepstrum for the set of frames; and

performing inverse exponential windowing on the set of frames.

19. The system of claim 15 , wherein the executable instructions cause the processor to extract the one or more features of the SEIR by:

obtaining a first SEIR feature of the one or more features as energy of multiple bands of initial samples of the SEIR; and

obtaining a second SEIR feature of the one or more features as an average of maxima indices of SEIR magnitude.

20. The system of claim 19 , wherein the executable instructions cause the processor to extract the one or more features of the SEIR by:

obtaining a third SEIR feature of the one or more features as a time kurtosis of the SEIR;

obtaining a fourth SEIR feature of the one or more features as a spectral standard deviation at a center frequency of the SEIR; and

obtaining a fifth SEIR feature of the one or more features as a slope of samples of the SEIR.