Multi-channel acoustic event detection and classification method
A method for a multi-channel acoustic event detection and classification for weak signals, operates at two stages; a first stage detects a power and probability of events within a single channel, accumulated events in the single channel triggers a second stage, wherein the second stage is a power-probability image generation and classification using tokens of neighbouring channels.
1. A method for a multi-channel acoustic event detection and classification, comprising the following steps of:
specifying a time window from raw acoustic signals, received from a multi-channel acoustic device in a synchronized fashion and stored in channel database,
computing a power of each channel of channels for a specified window size,
computing a classification probability of the raw acoustic signals for the time window,
computing a cross product of the power and the classification probability and storing the cross product as a third dimension of a power-probability image to enrich an information capacity, wherein a first dimension, a second dimension and the third dimension of the power-probability image are respectively the power, the classification probability and the cross product of the power and the classification the classification probability,
applying a convolutional neural network trained to detect spectrograms of acoustic events, denoted as a phoneme classifier, on the each channel independently,
counting high-probability events exceeding a given threshold independently for the each channel using probability information from the power-probability image to detect possible channels with the high-probability events,
recording the channels having a certain number of the high-probability events, exceeding the given threshold, to an event channel stack,
cropping a region of interest around every event of interest, wherein the every event of interest is determined by a user in the each channel in the event channel stack,
operating a power-probability classifier on accumulated results of phoneme classifier probabilities along with the power fora certain type of event classified by the phoneme classifier,
reporting an event when the power-probability classifier generates a result exceeding a threshold for the event to be declared.
2. The method according to claim 1 , comprising utilizing a synthetic activity generator to create possible event scenarios for a training along with actual data.
3. The method according to claim 1 , wherein the power of the each channel for the specified window size is computed by:
normalizing the power using a ratio of low-frequency components to high-frequency components,
clipping the power from a top and a bottom and quantizing to a power quantization level in between,
storing a quantized power in the power-probability image.
4. The method according to claim 1 , wherein a machine learning technique for computing the classification probability of the raw acoustic signals for the time window is the convolutional neural network.