Unsupervised Feature Learning for Audio Analysis
This addresses the problem of identifying unknown acoustic events in streaming audio for applications like environmental monitoring, representing an incremental improvement with specific gains.
The paper tackles unsupervised feature learning for audio analysis by introducing a Convolutional LSTM autoencoder for audio frame prediction and a training method that amplifies event similarities, resulting in 13% better classification and 36% better clustering performance compared to standard approaches.
Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method for exploration of audio data is presented in this paper. It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction. Second, a training method for autoencoders is presented, which leads to distinct features by amplifying event similarities. In comparison to standard approaches, the features extracted from the audio frame predictor trained with the novel approach show 13 % better results when used with a classifier and 36 % better results when used for clustering.