# US 11,308,979 B2 · Open vs enclosed spatial environment classification for a mobile or wearable device using microphone and deep learning method

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- Number: US 11,308,979 B2
- Document ID: US11308979B2
- Status: Granted
- Kind: B2
- Application number: 17/158,817
- Filed: 2021-01-26
- Published: 2021-05-20
- Granted: 2022-04-19
- Earliest claimed: 2019-01-07
- Assignees: STMicroelectronics S.r.l.; STMicroelectronics International N.V.
- Applicants: STMicroelectronics, Inc.; STMicroelectronics International N.V.
- Inventors: Mahesh Chowdhary; Arun Kumar; Ghanapriya Singh; Rajendar Bahl
- Web page: https://patentgrep.com/patent/US11308979B2

## CPC

- G10L 25/51: for comparison or discrimination
- G10L 25/21: the extracted parameters being power information
- G10L 25/24: the extracted parameters being the cepstrum
- H04M 1/72454: according to context-related or environment-related conditions
- H04R 1/406: {microphones}
- H04R 3/005: {for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407)}
- H04W 4/025: {using location based information parameters}
- H04W 4/029: Location-based management or tracking services
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08)
- H04R: LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS (producing sounds with frequency not determined by supply frequency G10K)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

A method and apparatus for classifying a spatial environment as open or enclosed are provided. In the method and apparatus, one or more microphones detect ambient sound in a spatial environment and output an audio signal representative of the ambient sound. A processor determines a spatial environment impulse response (SEIR) for the audio signal and extracts one or more features of the SEIR. The processor classifies the spatial environment as open or enclosed based on the one or more features of the SEIR.

## Claims

### Claim 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;
obtaining a plurality of frames of the audio signal, the plurality of frames including a first frame and a second frame;
determining an energy of the first frame and an energy of the second frame; and
classifying, by the processor, a type of the spatial environment based on the energy of the first frame and the energy of the second frame.

### Claim 2 (depends on 1)

The method of claim 1, wherein classifying the type of the spatial environment based on the energy of the first frame and the energy of the second frame includes classifying the type of the spatial environment based on a ratio of the energy of the first frame to the energy of the second frame.

### Claim 3 (depends on 1)

The method of claim 1, comprising:
determining a spatial environment impulse response (SEIR) for the audio signal by at least:
selecting, from the plurality of frames, a set of frames having respective energy ratios that meet a criterion, the set of frames including at least one of the first frame and the second frame;
performing 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.

### Claim 4 (depends on 3)

The method of claim 3, wherein classifying the type of the spatial environment based on the energy of the first frame and the energy of the second frame includes classifying the type of the spatial environment based on one or more features of the SEIR.

### Claim 5 (depends on 4)

The method of claim 4, comprising:
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 based on the composite vector.

### Claim 6 (depends on 1)

The method of claim 1, wherein the type of the spatial environment is an open spatial environment or an enclosed spatial environment.

### Claim 7 (depends on 1)

The method of claim 1, wherein the type of the spatial environment is at least one of: an office, home, mall, supermarket, street, stadium, beach or nature.

### Claim 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:
obtain a plurality of frames of the audio signal, the plurality of frames including a first frame and a second frame;
determine an energy of the first frame and an energy of the second frame; and
classify a type of the spatial environment based on the energy of the first frame and the energy of the second frame.

### Claim 9 (depends on 8)

The device of claim 8, wherein classifying the type of the spatial environment based on the energy of the first frame and the energy of the second frame includes classifying the type of the spatial environment based on a ratio of the energy of the first frame to the energy of the second frame.

### Claim 10 (depends on 8)

The device of claim 8, wherein the processor is configured to:
determine a spatial environment impulse response (SEIR) for the audio signal by at least:
selecting, from the plurality of frames, a set of frames having respective energy ratios that meet a criterion, the set of frames including at least one of the first frame and the second frame;
performing 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.

### Claim 11 (depends on 10)

The device of claim 10, wherein classifying the type of the spatial environment based on the energy of the first frame and the energy of the second frame includes classifying the type of the spatial environment based on one or more features of the SEIR.

### Claim 12 (depends on 11)

The device of claim 11, wherein the processor is configured to:
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 based on the composite vector.

### Claim 13 (depends on 8)

The device of claim 8, wherein the type of the spatial environment is an open spatial environment or an enclosed spatial environment.

### Claim 14 (depends on 8)

The device of claim 8, wherein the type of the spatial environment is at least one of: an office, home, mall, supermarket, street, stadium, beach or nature.

### Claim 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;
obtain a plurality of frames of the audio signal, the plurality of frames including a first frame and a second frame;
determine an energy of the first frame and an energy of the second frame; and
classify a type of the spatial environment based on the energy of the first frame and the energy of the second frame.

### Claim 16 (depends on 15)

The system of claim 15, wherein classifying the type of the spatial environment based on the energy of the first frame and the energy of the second frame includes classifying the type of the spatial environment based on a ratio of the energy of the first frame to the energy of the second frame.

### Claim 17 (depends on 15)

The system of claim 15, wherein the processor is configured to:
determine a spatial environment impulse response (SEIR) for the audio signal by at least:
selecting, from the plurality of frames, a set of frames having respective energy ratios that meet a criterion, the set of frames including at least one of the first frame and the second frame;
performing 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.

### Claim 18 (depends on 17)

The system of claim 17, wherein classifying the type of the spatial environment based on the energy of the first frame and the energy of the second frame includes classifying the type of the spatial environment based on one or more features of the SEIR.

### Claim 19 (depends on 18)

The system of claim 18, wherein the processor is configured to:
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 based on the composite vector.

### Claim 20 (depends on 15)

The system of claim 15, wherein the type of the spatial environment is an open spatial environment or an enclosed spatial environment.