Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context

Pham, Lam; Salovic, Dusan; Jalali, Anahid; Strobel, Alexander; Tran, Khoa D.; Vu, Canh; Nguyen, Phu X.

doi:10.48550/arxiv.2210.08610

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Regarding the teacher architecture, it comprises two main parts: a CNN-based backbone followed by a dense block. The CNN-based backbone, which presents a residual-inception based architecture, is reused from [21], [10]. The dense block comprises two dense layers (Dense Layer 01 and Dense Layer 02), which is shown in the lower part of Fig.…”

Section: The Teacher Network Architecturementioning

confidence: 99%

A Low-Complexity Deep Learning Framework For Acoustic Scene Classification

Pham

Tang

Jalali

et al. 2022

Data Science – Analytics and Applications

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In this technical report, a low-complexity deep learning system for acoustic scene classification (ASC) is presented. The proposed system comprises two main phases: (Phase I) Training a teacher network; and (Phase II) training a student network using distilled knowledge from the teacher. In the first phase, the teacher, which presents a large footprint model, is trained. After training the teacher, the embeddings, which are the feature map of the second last layer of the teacher, are extracted. In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher. Our experiments conducted on DCASE 2023 Task 1 Development dataset have fulfilled the requirement of low-complexity and achieved the best classification accuracy of 57.4%, improving DCASE baseline by 14.5%.

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Section: The Teacher Network Architecturementioning

confidence: 99%

A Low-Complexity Deep Learning Framework For Acoustic Scene Classification

Pham

Tang

Jalali

et al. 2022

Data Science – Analytics and Applications

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show abstract

“…Regarding the residual-inception based network for training audio spectrograms, it is separated into two main parts: A Residual-Inception block and a Dense block. The Residual-Inception block in this paper is the CNN-based backbone of the novel residual-inception deep neural network architecture which is reused from our previous works in [15]. Meanwhile, with RGB format.…”

Section: A Phase I: Train Deep Learning Models On Individual Audio Or...mentioning

confidence: 99%

Proposed Smart University Model as a Sustainable Living Lab for University Digital Transformation

Tuấn

Nguyen

Ngo

et al. 2020

2020 5th International Conference on Green Technology and Sustainable Development (GTSD)

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In this paper, we present a deep learning based multimodal system for classifying daily life videos. To train the system, we propose a two-phase training strategy. In the first training phase (Phase I), we extract the audio and visual (image) data from the original video. We then train the audio data and the visual data with independent deep learning based models. After the training processes, we obtain audio embeddings and visual embeddings by extracting feature maps from the pretrained deep learning models. In the second training phase (Phase II), we train a fusion layer to combine the audio/visual embeddings and a dense layer to classify the combined embedding into target daily scenes. Our extensive experiments, which were conducted on the benchmark dataset of DCASE (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) 2021 Task 1B Development, achieved the best classification accuracy of 80.5%, 91.8%, and 95.3% with only audio data, with only visual data, both audio and visual data, respectively. The highest classification accuracy of 95.3% presents an improvement of 17.9% compared with DCASE baseline and shows very competitive to the state-of-the-art systems.

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Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context

Cited by 2 publications

References 24 publications

A Low-Complexity Deep Learning Framework For Acoustic Scene Classification

A Low-Complexity Deep Learning Framework For Acoustic Scene Classification

Proposed Smart University Model as a Sustainable Living Lab for University Digital Transformation

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