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In acoustic scene classification (ASC), acoustic features play a crucial role in the extraction of scene information, which can be stored over different time scales. Moreover, the limited size of the dataset may lead to a biased model with a poor performance for records from unseen cities and confusing scene classes. In order to overcome this, we propose a long-term wavelet feature that requires a lower storage capacity and can be classified faster and more accurately compared with classic Mel filter bank coefficients (FBank). This feature can be extracted with predefined wavelet scales similar to the FBank. Furthermore, a novel data augmentation scheme based on generative adversarial neural networks with auxiliary classifiers (ACGANs) is adopted to improve the generalization of the ASC systems. The scheme, which contains ACGANs and a sample filter, extends the database iteratively by splitting the dataset, training the ACGANs and subsequently filtering samples. Experiments were conducted on datasets from the Detection and Classification of Acoustic Scenes and Events (DCASE) challenges. The results on the DCASE19 dataset demonstrate the improved performance of the proposed techniques compared with the classic FBank classifier. Moreover, the proposed fusion system achieved first place in the DCASE19 competition and surpassed the top accuracies on the DCASE17 dataset.
In this paper, we present SpecAugment++, a novel data augmentation method for deep neural networks based acoustic scene classification (ASC). Different from other popular data augmentation methods such as SpecAugment and mixup that only work on the i
In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Challenge. Task 1 comprises two different sub-tasks: (i) Task 1a focuses o
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 cla
In this paper, the Brno University of Technology (BUT) team submissions for Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2018 challenge are described. Also, the analysis of different methods on the leaderboard set is provided. The propose
In recent years, deep learning technique has received intense attention owing to its great success in image recognition. A tendency of adaption of deep learning in various information processing fields has formed, including music information retrieva