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Time-frequency matrix dynamic selection based sound event classification method and system

A classification method and time-frequency technology, applied in speech analysis, computer components, instruments, etc., can solve the problems of inability to select time-frequency matrix for sound signals, complexity, and large amount of calculation, and achieve reduction of calculation amount, simple implementation, and increase The effect of large high-frequency resolution

Active Publication Date: 2020-02-04
SHANDONG UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The inventors found that in the existing image signal feature extraction method, the pure sound and the noise-containing sound adopt different feature extraction processes, which is relatively complicated; and all the sound signals are uniformly adjusted into a fixed-size time-frequency matrix, which cannot be targeted Selecting a suitable time-frequency matrix for sound signals of different types and lengths requires a large amount of calculation

Method used

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  • Time-frequency matrix dynamic selection based sound event classification method and system
  • Time-frequency matrix dynamic selection based sound event classification method and system
  • Time-frequency matrix dynamic selection based sound event classification method and system

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Embodiment 1

[0041] There are various applications of sound scene recognition and classification, including intelligent environment scene recognition, sound-based monitoring systems, home automation, machine hearing and so on. This sound event classification method can be applied to environmental scene recognition applications, various sound detection systems, home automation and other applications, combined with specific application scenarios, to achieve corresponding operations. Taking sound monitoring by sound event classification as an example, the monitoring system recognizes and classifies various sounds collected in real time to determine dangerous events, and then can take countermeasures, such as gunfire, explosions, etc., which will trigger the alarm system; For example, in the auditorium, the light color and type are automatically changed according to the recognized applause sound; in home automation, each smart home automatically performs pre-set operations according to the diffe...

Embodiment 2

[0073] In one or more embodiments, a sound event classification system based on a time-frequency matrix dynamic selection is disclosed, including:

[0074] A device for collecting sound signal data in a set area environment and preprocessing the sound signal data;

[0075] A device for generating a spectrogram for the preprocessed sound signal;

[0076] A device used to gradually reduce the original spectrogram to generate multiple time-frequency matrices of different sizes;

[0077] A device used to find the similarity between each time-frequency matrix and the original spectrogram, find the optimal time-frequency matrix, and convert the image into a picture signal;

[0078] A device used to extract characteristic events from image signals; send the extracted characteristic events to the classifier to obtain the result of sound event classification.

Embodiment 3

[0080] In one or more embodiments, a terminal device is disclosed, including a server, the server including a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor executes the The program implements the sound event classification method based on the time-frequency matrix dynamic selection in the first embodiment. For the sake of brevity, I will not repeat them here.

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Abstract

The invention discloses a time-frequency matrix dynamic selection based sound event classification method and system. The time-frequency matrix dynamic selection based sound event classification method comprises steps of collecting sound signal data in a set regional environment and preconditioning the sound signal data; generating a spectrogram for the preconditioned sound signals; zooming out the original spectrogram gradually to generate multiple time-frequency matrices different in size; obtaining similarity of each time-frequency matrix and the original spectrogram to find an optimal time-frequency matrix and converting the optimal time-frequency matrix to graph signals; extracting characteristic events from the graph signals; and sending the extracted characteristic events to a classifier to obtain a classification result of the sound events. The time-frequency matrix dynamic selection based sound event classification method has beneficial effects that the characteristic extraction process is simplified and a suitable dynamic threshold value is arranged to ensure completeness of the extracted characteristics. As a graph signal based method is adopted for similarity calculation of two images and a time-frequency matrix is selected dynamically for each sound signal, high-energy spectrum information of sound signals can be retained as far as possible while the calculation amount is reduced.

Description

Technical field [0001] The invention belongs to the technical field of sound recognition and classification, and in particular relates to a sound event classification method and system based on a time-frequency matrix dynamic selection. Background technique [0002] There are various sound scenes around us in daily life, including the sound of objects colliding with each other, the sound of gas jets, the sound of particles falling, the sound of objects rubbing, various bells, horns, and electronic musical instruments. Sound, etc., the recognition and classification of nonverbal sound events in the real environment has attracted more and more people's attention. There are various applications of sound scene recognition and classification, including intelligent environment scene recognition, sound-based monitoring systems, home automation, machine hearing and so on. [0003] Nowadays, speech recognition technology is very common, but the recognition and classification of sound scene...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L17/26G10L25/18G06K9/00
CPCG10L17/26G10L25/18G06F2218/08G06F2218/12
Inventor 魏莹刘迎港
Owner SHANDONG UNIV
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