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Sound event detection model training method and sound event detection method

A technology of event detection and model training, applied in biological neural network models, voice analysis, instruments, etc., can solve the problem that the network cannot directly learn and post-process, and achieve the effect of reducing difficulty and improving accuracy

Inactive Publication Date: 2019-09-10
AISPEECH CO LTD
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Problems solved by technology

However, the network cannot directly learn post-processing

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  • Sound event detection model training method and sound event detection method
  • Sound event detection model training method and sound event detection method
  • Sound event detection model training method and sound event detection method

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Embodiment Construction

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described clearly and completely in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments It is a part of the embodiments of this application, but not all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of this application.

[0024] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other if there is no conflict.

[0025] This application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, elements, da...

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Abstract

The invention discloses a sound event detection model training method. A sound event detection model comprises a convolutional neural network and a circulatory neural network. The sound event detection model training method comprises the steps that a downsampling layer is arranged in at least one of a plurality of convolutional layers of the convolutional neural network, and the downsampling layeris used for downsampling time resolution of neurons of the convolutional neural network; and the circulatory neural network is arranged and used for classifying sound events according to sound feature information output by the convolutional neural network. According to the sound event detection model training method, through downsampling of the time resolution of neuron layer surfaces is carriedout when the sound feature information of the convolutional neural network is extracted, so that sound feature information with clearer an event boundary is obtained, the sound events are convenientlyclassified by the subsequent circulatory neural network, the accuracy of the sound event classification is improved, and the difficulty of sound event classification is reduced.

Description

Technical field [0001] This application relates to the field of artificial intelligence technology, and in particular to a sound event detection model training method and a sound event detection method. Background technique [0002] With the popularity of artificial intelligence and deep neural networks in the fields of image, video, and voice, there are more and more applications for AI in the audio field, including scene classification, audio event detection, and network audio and video. DCASE (Detection and Classification of Acoustic Scenes and Events) is the abbreviation for the classification and detection of audio scenes and audio events. DCASE has a wide range of application scenarios, such as smart home, unmanned driving, and voice recognition in complex scenarios. [0003] SED (Sound Event Detection) is essentially a semi-supervised duration estimation problem, which means that hard tags (time stamps) are not available during training. However, due to the lack of prior k...

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Application Information

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IPC IPC(8): G10L25/51G10L25/30G06N3/04
CPCG10L25/51G10L25/30G06N3/044G06N3/045
Inventor 俞凯丁翰林
Owner AISPEECH CO LTD
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