# US 2022/0044698 A1 · Acoustic event detection system and method

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- Number: US 2022/0044698 A1
- Document ID: US20220044698A1
- Status: Published application
- Kind: A1
- Application number: 17/356,696
- Filed: 2021-06-24
- Published: 2022-02-10
- Earliest claimed: 2020-08-04
- Assignees: Realtek Semiconductor Corp.
- Applicants: Realtek Semiconductor Corp.
- Inventors: HUNG-PIN HUANG
- Web page: https://patentgrep.com/patent/US20220044698A1

## CPC

- G10L 25/78: Detection of presence or absence of voice signals (switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10)
- G06N 20/00: Machine learning
- G10L 15/08: Speech classification or search
- G10L 21/0224: Processing in the time domain
- G10L 25/24: the extracted parameters being the cepstrum
- G10L 25/27: characterised by the analysis technique
- G10L 25/51: for comparison or discrimination
- G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L 2015/088: {Word spotting}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

An acoustic event detection system and a method are provided. The system includes a voice activity detection subsystem, a database, and an acoustic event detection subsystem. The voice activity detection subsystem includes a voice receiving module, a feature extraction module, and a first determination module. The voice receiving module receives an original sound signal, the feature extraction module extracts a plurality of features from the original sound signal, and the first determination module executes a first classification process to determine whether or not the plurality of features match to a start-up voice. The acoustic event detection subsystem includes a second determination module and a function response module. The second determination module executes a second classification process to determine whether the features match to at least one of a plurality of predetermined voices. The function response module executes one of functions corresponding to the predetermined voices that is matched.

## Claims

### Claim 1

An acoustic event detection system, comprising:
a voice activity detection subsystem, including:
a voice receiving module configured to receive an original sound signal;
a feature extraction module configured to extract a plurality of features from the original sound signal; and
a first determination module configured to execute a first classification process to determine whether or not the plurality of features match to a start-up voice;
a database configured to store the plurality of extracted features; and
an acoustic event detection subsystem, including:
a second determination module configured to, in response to the first determination module determining that the plurality of features match the start-up voice, execute a second classification process to determine whether or not the plurality of features match to at least one of a plurality of predetermined voices; and
a function response module configured to, in response to the second determination module determining that the plurality of features match at least one of the plurality of predetermined voices, execute one of a plurality of functions corresponding to the at least one of the plurality of predetermined voices that is matched.

### Claim 2 (depends on 1)

The acoustic event detection system according to claim 1, wherein the plurality of features are a plurality of Mel-Frequency Cepstral Coefficients (MFCCs).

### Claim 3 (depends on 2)

The acoustic event detection system according to claim 2, wherein the feature extraction module extracts the plurality of features of the original sound signal through an extraction process, and the extraction process includes:
decomposing the original sound signal into a plurality of frames;
pre-enhancing signal data corresponding to the plurality of frames through a high-pass filter;
performing a Fourier transformation to convert the pre-enhanced signal data to a frequency domain to generate a plurality of sets of spectrum data corresponding to the plurality of frames;
obtaining a plurality of mel scales by applying a mel filter on the plurality of sets of spectrum data;
extracting logarithmic energy on the plurality of mel scales; and
performing a discrete cosine transformation on the obtained logarithmic energy to convert to a cepstrum domain, so as to generate the plurality of Mel-Frequency Cepstral Coefficients.

### Claim 4 (depends on 3)

The acoustic event detection system according to claim 3, wherein the first classification process includes comparing the plurality of sets of spectrum data with spectrum data of the start-up voice to determine whether the plurality of features match to the start-up voice.

### Claim 5 (depends on 1)

The acoustic event detection system according to claim 1, wherein the second classification process includes identifying the plurality of features through a trained machine learning model to determine whether the plurality of features match to at least one of the plurality of predetermined voices.

### Claim 6

An acoustic event detection method, comprising:
configuring a voice receiving module of a voice activity detection subsystem to receive an original sound signal;
configuring a feature extraction module of the voice activity detection subsystem to extract a plurality of features from the original sound signal;
configuring a first determination module of the voice activity detection subsystem to execute a first classification process and determine whether or not the plurality of features match to a start-up voice; and
storing the plurality of extracted features in a database;
wherein in response to the first determination module determining that the plurality of features match the start-up voice, configuring a second determination module of an acoustic event detection subsystem to execute a second classification process to determine whether or not the plurality of features match to at least one of a plurality of predetermined voices;
wherein in response to the second determination module determining that the plurality of features match at least one of the plurality of predetermined voices, configuring a function response module of the acoustic event detection subsystem to execute one of a plurality of functions corresponding to the at least one of the plurality of predetermined voices that is matched.

### Claim 7 (depends on 6)

The acoustic event detection method according to claim 6, wherein the plurality of features are a plurality of Mel-Frequency Cepstral Coefficients (MFCCs).

### Claim 8 (depends on 7)

The acoustic event detection method according to claim 7, wherein the feature extraction module extracts the plurality of features of the original sound signal through an extraction process, and the extraction process includes:
decomposing the original sound signal into a plurality of frames;
pre-enhancing signal data corresponding to the plurality of frames through a high-pass filter;
performing a Fourier transformation to convert the pre-enhanced signal data to a frequency domain to generate a plurality of sets of spectrum data corresponding to the plurality of frames;
obtaining a plurality of mel scales by applying a mel filter on the plurality of sets of spectrum data;
extracting a logarithmic energy on the plurality of mel scales; and
performing a discrete cosine transformation on the obtained logarithmic energy to convert to a cepstrum domain, so as to generate the plurality of Mel-Frequency Cepstral Coefficients.

### Claim 9 (depends on 8)

The acoustic event detection method according to claim 8, wherein the first classification process includes comparing the plurality of sets of spectrum data with spectrum data of the start-up voice to determine whether the plurality of features match to the start-up voice.

### Claim 10 (depends on 6)

The acoustic event detection method according to claim 6, wherein the second classification process includes identifying the plurality of features through a trained machine learning model to determine whether the plurality of features match to at least one of the plurality of predetermined voices.