Sound event detection method based on convolutional neural network

Abstract

The invention discloses a sound event detection method based on a convolutional neural network, and belongs to the technical field of audio processing. The method comprises the steps: firstly, performing primary feature extraction on an audio stream; then sending the extracted primary feature to a neural network for feature extraction and classification of sound events, and finally obtaining prediction probabilities of various sound events; and if the prediction probability of the current type of sound event exceeds a preset classification threshold, considering that a corresponding sound event exists in the current audio stream. A sound event detection model is small in parameter quantity and low in calculation complexity, so that the power consumption and the calculation complexity of the related Internet of Things equipment during sound detection processing are greatly reduced; and the detection precision equivalent to that of an existing sound event detection model is maintained. Therefore, the sound event detection method provided by the invention can be effectively applied to embedded intelligent equipment and the like.

Description

technical field [0001] The invention belongs to the technical field of audio processing, and in particular relates to a sound event detection technology based on a convolutional neural network. Background technique [0002] Sound event detection means that the device detects one or more types of sound events (collectively referred to as multiple sound events) that exist at the current moment from the continuous audio stream. Sound event detection (SED) technology has been widely used in smart home, video surveillance, environmental monitoring and other fields. For example, in a smart home application, SED technology can be used to detect a baby crying and notify the parents in the kitchen. In video surveillance applications, SED technology can be used to trigger video surveillance when abnormal sound events such as gunshots or screams are detected, thereby realizing video surveillance driven by sound events and greatly reducing power consumption. In environmental monitorin...

AI Technical Summary

Problems solved by technology

[0004] How to improve the detection accuracy of SED is the focus of the existing SED technology research, but the detection accuracy of the sound event detection method based on traditional machine learning is limited; while the sound event detection method based on neural network can improve the detection accuracy, but There are limitations: that is, the number of deep neural network parameters is large, the calculation complexity is high, the storage space required is large, and the power consumption is large, which makes it unsuitable for IoT devices with severe power consumption and resource constraints.

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