Automatic audio device self-checking method based on embedded telephone equipment

The automatic audio component self-test method of embedded phone equipment, combined with spectrum similarity and loudness detection, solves the problems of low efficiency and poor accuracy of audio component detection, and realizes fast and accurate audio component detection and fault diagnosis.

CN120711340APending Publication Date: 2025-09-26BEIJING FANGWEI ZHILIAN TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510934073.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-08
Publication Date
2025-09-26

AI Technical Summary

Technical Problem

Existing technologies make it difficult to achieve efficient and accurate audio component detection in audio equipment production and maintenance, especially in determining the working status of microphones and speakers. This is greatly affected by subjective factors and environmental noise, and the complex system-level coupling characteristics make detection cumbersome.

Method used

An automatic audio device self-test method based on embedded telephone equipment is adopted. Through spectrum similarity, single frequency detection and loudness anomaly detection, combined with frequency domain characteristics and loudness indicators, the Goertzel algorithm is used to analyze audio signals to achieve automatic detection and fault diagnosis of audio devices.

Benefits of technology

It achieves fast and accurate detection of audio components, can effectively locate abnormal links, improve the comprehensiveness and accuracy of detection, adapt to the customized needs of different models and scenarios, support remote startup and batch deployment, and has the ability to suppress noise interference.

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Abstract

The invention discloses an automatic audio device self-checking method based on embedded telephone equipment, and relates to the field of audio signal processing and hardware diagnosis. The method comprises the steps that firstly, a microphone collects first 3S playback signals of a test audio, and frequency similarity detection is carried out by extracting spectrum features; then, continuously collecting the 4s-5s playback signal of the audio, and carrying out single frequency point detection; meanwhile, aiming at the front 3S playback signals, calculating a root-mean-square RMS value according to signal energy, converting the RMS value into loudness, and carrying out loudness anomaly detection; thirdly, continuously collecting the 6s-7s playback signal of the audio for sampling analysis, calculating the root-mean-square of the playback signal, converting the root-mean-square into loudness L2, and when the loudness value L2 is lower than a set threshold value, indicating that the MIC device is abnormal; and finally, through frequency similarity detection, single-frequency detection, loudness anomaly detection and device anomaly detection, results of four rounds of detection are reported in a binary form, and equipment sound anomaly reasons are analyzed. According to the invention, specific abnormal links in the audio link can be effectively positioned.
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