Acoustic Scene Classification Using Teacher-Student Learning with Soft-Labels

Abstract

Acoustic scene classification identifies an input segment into one of the pre-defined classes using spectral information. The spectral information of acoustic scenes may not be mutually exclusive due to common acoustic properties across different classes, such as babble noises included in both airports and shopping malls. However, conventional training procedure based on one-hot labels does not consider the similarities between different acoustic scenes. We exploit teacher-student learning with the purpose to derive soft-labels that consider common acoustic properties among different acoustic scenes. In teacher-student learning, the teacher network produces softlabels, based on which the student network is trained. We investigate various methods to extract soft-labels that better represent similarities across different scenes. Such attempts include extracting soft-labels from multiple audio segments that are defined as an identical acoustic scene. Experimental results demonstrate the potential of our approach, showing a classification accuracy of 77.36 % on the DCASE 2018 task 1 validation set.

Knowledge Distillation in Acoustic Scene Classification

“…Knowledge distillation was first devised in two studies conducted by Hinton et al and Li et al [25], [26] and was introduced for the ASC task in [24]. Although the two papers convey similar methodologies, Hinton et al focused on the transfer of knowledge between DNNs, therefore, referred to as knowledge distillation.…”

“…The KD framework itself has been validated to be effective for different input features (e.g. Mel-spectrogram) in previous works [24], [27]. Table 3 describes comparison results with different composition of loss functions for knowledge distillation.…”

“…Common acoustic properties that reside among different acoustic scenes cause performance degradation of the ASC task [24]. It is hypothesized that the learning of a DNN can be disturbed if audio segments are labelled differently even though they share acoustic properties.…”

“…In this study, we adopt a knowledge distillation (KD) framework [25], [26] from the perspective of extracting soft labels that reflect the similarity between defined acoustic scenes, which was first introduced to the ASC task in authors' previous work [24]. The KD framework sequentially trains two DNNs, where the first is trained using conventional ground truth labels (i.e.…”

“…In this study, we present an overall adaptation of the KD framework for the ASC task with hypothesis and problematic phenomena in which our hypothesis evokes [24], [27]. The novelty of this paper can be summarized as follows:…”