Bag-of-Audio-Words based on Autoencoder Codebook for Continuous Emotion Prediction
This work addresses emotion prediction in audio processing, offering an incremental improvement over conventional methods for researchers in affective computing.
The paper tackles continuous emotion prediction from audio by proposing a Bag-of-Audio-Words representation using an autoencoder codebook, which improved the Concordance Correlation Coefficient from 0.225 to 0.322 for arousal and from 0.244 to 0.368 for valence on the AVEC 2017 dataset.
In this paper we present a novel approach for extracting a Bag-of-Words (BoW) representation based on a Neural Network codebook. The conventional BoW model is based on a dictionary (codebook) built from elementary representations which are selected randomly or by using a clustering algorithm on a training dataset. A metric is then used to assign unseen elementary representations to the closest dictionary entries in order to produce a histogram. In the proposed approach, an autoencoder (AE) encompasses the role of both the dictionary creation and the assignment metric. The dimension of the encoded layer of the AE corresponds to the size of the dictionary and the output of its neurons represents the assignment metric. Experimental results for the continuous emotion prediction task on the AVEC 2017 audio dataset have shown an improvement of the Concordance Correlation Coefficient (CCC) from 0.225 to 0.322 for arousal dimension and from 0.244 to 0.368 for valence dimension relative to the conventional BoW version implemented in a baseline system.