The invention discloses a sound information conversion method, a device and equipment and relates to the field of sound recognition and conversion. The invention mainly aims to automatically and conditionally convert a sound message received by a user into a text message. According to the technical scheme of the invention, the method comprises the steps of receiving a user operation, wherein the user operation is used for indicating a first electronic device to present a first message and the content of the first message comprises the first sound content in the audio format; judging the presentation mode of the first message, and obtaining a judgment result; if the judgment result is a first judgment result, controlling an image display module to output a first message identifier, and enabling an audio output module to output the first sound content; if the judgment result is a second judgment result, converting the first sound content into the non-audio format, and controlling the image display module to output the first sound content in the non-audio format. The sound information conversion method, the device and the equipment are mainly used for converting the sound information.

Sound information conversion method, device and equipment

The invention discloses a supersonic wave based partial discharge detection method. The method comprises the steps of supersonic signal collection and pre-processing, abnormal signal interval detection, characteristic vector extraction, partial least squares based dimension reduction optimization, and vector machine based fault and faultless classification. A supersonic wave detection method is used to carry out partial discharge detection on a switchgear, a supersonic signal is analyzed and processed to predict an insulation state of the switchgear; an audio signal processing technology and amode identification classification algorithm are used to carry out down-conversion on the supersonic signal, and a signal within the voice frequency range is obtained; a signal characteristic parameter representing partial discharge is extracted; via partial least squares based dimension reduction optimization, the complex degree of the algorithm is reduced; and further advance voice identification technology and mathematical classification model to realize intelligent identification. Thus, partial discharge condition in the switchgear can be detected online intelligently in a non-intruding way, and the insulation condition of the switchgear is detected and diagnosed.

Supersonic wave based partial discharge detection method

The invention discloses a sound identification method. The sound identification method comprises steps that sound signals are acquired, and the retention time of each pulse of the sound signals is determined; based on the retention time, frequencies of the sound signals are calculated; the frequencies of the sound signals are compared with preset reference frequency, similarities between the frequencies of the sound signals and the preset reference frequency are determined, and a sound type corresponding to the largest preset reference frequency is determined as the type of the acquisition sound on the condition that similarity is greater than the preset threshold. The invention further discloses a sound identification device.

Sound identification method and device

The invention discloses a method for detecting internal defects of a brake pad by analyzing a sound signal, and the method comprises the following steps: acquiring the sound signal; intercepting and preprocessing the sound signal; carrying out wavelet time-frequency analysis; extracting the characteristic of a gray level image; inputting the characteristic quantity into a neural network for training; judging the internal defects of the brake pad. The method is scientific and reasonable, and is high in diagnosis accuracy.

Method for detecting internal defects of brake pad by analyzing sound signal

The invention discloses a power transmission line bird repelling method based on unmanned aerial vehicle inspection, and the method comprises the steps: setting the sound collection frequency of a microphone, and collecting a sound signal in a set range of a power transmission line through the microphone carried by an unmanned aerial vehicle; Setting the image shooting resolution of the camera, and acquiring images in a set range of the power transmission line by using the camera carried by the unmanned aerial vehicle; Processing the sound signal, inputting the processed sound signal into a pre-trained first classifier, and outputting a first classification result; Processing the image, inputting the image into a pre-trained second classifier, and outputting a second classification result;Judging whether the first classification result is consistent with the second classification result or not, if yes, judging that birds exist near the power transmission line, driving the unmanned aerial vehicle to execute a set flight action, and driving the birds; And if not, returning to the step of setting the sound collection frequency of the microphone and the image shooting resolution of the camera at the same time.

A power transmission line bird repelling method based on unmanned aerial vehicle inspection

The invention discloses a natural environment recognition method based on time-frequency domain statistical feature extraction. The method comprises the steps of (1) collecting various types of natural environmental sounds such as the sound of an engine, a car horn and wind noise, and establishing a sound sample library, (2) carrying out window framing processing on a sound sample signal, (3) extracting the statistical features of all frame signal in a time domain, (4) marking the type of a sound source to which feature vectors belong, and establishing a sample feature library, (5) training a feature vector by using a support vector machine, and establishing a training model, (6) extracting the feature vectors of a target sound, (7) carrying out matching classification on the feature vectors of the target sound by using the support vector machine, and (8) providing an recognition result. According to the method, the disadvantages of traditional sound LPCC and MFCC feature extraction methods in the aspect of time and frequency combination are made up, and the type to which various types of sounds belong can be judged.

Natural environment sound recognition method based on time-frequency domain statistical feature extraction

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Natural environment sound recognition method based on time-frequency domain statistical feature extraction