An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances
Table 2: Evaluation results on DCASE 2018 Task1a data set.
['Model', 'Accuracy %']
For a comprehensive evaluation, the high-resolution attention network with ASM (HRAN-ASM) system proposed in [ustc2] is also implemented, and its classification results are reported. In particular, we have adopted the two attention modules with ASM embedding into our AlexNet-L baseline model. The AlexNet-L attains a classification accuracy of 68.0%, which is improved to 69.5% by with the HRAN-ASM in the third row. Although our experiments are conducted with the same data sets adopted in [ustc2], experimental results are slightly different because of different speech features and models used in our work. We can use our acoustic segment blocking approach with three different ASM tokenizers, namely (i) the initial unsupervised ASM (initial ASM), (ii) GMM-HMM, and (iii) DNN-HMM. In case (i), stop ASMs are first detected using the tokenization obtained with initial ASM. Since the segment length of initial ASM sequences and the input sequences to AlexNet-L are the same, we can directly block the whole segment of the input sequence if its corresponding ASM token is in the set of stop ASMs. Thus, the blocking operation is based on the segment level, in which the re-segmenting and padding is not needed on processed segments. Although initial ASMs are simple models, AlexNet-L accuracy can be boosted from 68.0% to 70.1% (compare first and third rows). In details, GMM-HMM boosts AlexNet-L classification up to 71.6%. DNN-HMM can deliver more accurate alignments and boundaries for each ASM segment, which leads to a final scene classification accuracy of 72.1%, which representing a 4.1% absolute improvement when compared to AlexNet-L.