Lightweight Deep Space Separable Distillation Network for Efficient Acoustic Scene Classification

Alapfogalmak

A lightweight deep learning network architecture with efficient operators for high-performance acoustic scene classification.

Kivonat

The paper proposes a Deep Space Separable Distillation Network (DSSDN) for efficient acoustic scene classification. The key contributions are:

1. Frequency axis cutting: The network performs high-low frequency decomposition on the log-mel spectrogram, reducing computational complexity while maintaining model performance.

2. Lightweight operators: Three new lightweight operators are designed - Separable Convolution (SC), Orthonormal Separable Convolution (OSC), and Separable Partial Convolution (SPC). These operators exhibit efficient feature extraction capabilities for acoustic scene classification tasks.

3. Network architecture: The DSSDN architecture is built using the proposed DSSDB (Deep Space Separable Distillation Block) as the basic module, which stacks the DSSO (Deep Space Separable Operator) blocks. The channel splicing technique is used to fuse information from high-level and low-level networks.

The experiments demonstrate that the proposed DSSDN-Large, DSSDN-Middle, and DSSDN-Small models achieve significant performance gains of 9.8% compared to popular deep learning methods, while also having smaller parameter count and computational complexity.

Statisztikák

The proposed DSSDN-Large, DSSDN-Middle, and DSSDN-Small models have parameter counts of 0.11M, 0.11M, and 0.08M respectively, and MACs of 0.66G, 0.61G, and 0.56G respectively.
The accuracy of the three models is 66.20%, 65.63%, and 65.26% respectively.

Idézetek

"We design three lightweight operators specifically for the task characteristics of acoustic scene classification, and build three lightweight networks using the three operators as basic units."
"Compared with traditional lightweight networks, the lightweight operator we design can better meet the task requirements of acoustic scene classification and has better performance."

Főbb Kivonatok

Deep Space Separable Distillation for Lightweight Acoustic Scene Classification

by ShuQi Ye,Yua... : arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03567.pdf

Mélyebb kérdések

How can the proposed DSSDN architecture be further optimized to achieve even higher performance while maintaining its lightweight nature

To further optimize the DSSDN architecture for higher performance while preserving its lightweight nature, several strategies can be implemented. Firstly, exploring more advanced distillation techniques within the DSSDB module could enhance feature extraction efficiency. Introducing attention mechanisms like self-attention or transformer blocks could help the model focus on crucial features, improving classification accuracy. Additionally, incorporating residual connections between DSSDB modules can facilitate better information flow and gradient propagation, leading to enhanced performance. Moreover, experimenting with different activation functions or normalization techniques within the DSSO operators may contribute to better model convergence and overall effectiveness. Fine-tuning hyperparameters such as learning rate schedules and regularization methods can also play a vital role in optimizing the DSSDN architecture for superior performance.

What other applications beyond acoustic scene classification could benefit from the efficient feature extraction capabilities of the DSSO operators

The efficient feature extraction capabilities of the DSSO operators can benefit various applications beyond acoustic scene classification. One such application is environmental sound recognition, where identifying specific sounds in natural surroundings is crucial. The DSSO operators can help in extracting relevant features from audio data, enabling accurate classification of environmental sounds like bird calls, water streams, or animal noises. In addition, the operators can be utilized in medical imaging tasks such as MRI or CT scan analysis for efficient feature extraction from medical images, aiding in disease diagnosis and treatment planning. Furthermore, in natural language processing tasks like sentiment analysis or text classification, the DSSO operators can assist in extracting essential linguistic features from textual data, enhancing the performance of NLP models.

How can the frequency axis cutting technique be generalized to other types of input data beyond log-mel spectrograms to enable lightweight deep learning in diverse domains

The frequency axis cutting technique employed in the DSSDN architecture can be generalized to various types of input data beyond log-mel spectrograms to enable lightweight deep learning in diverse domains. For image data, this technique can be adapted by segmenting the spatial dimensions of the input image based on frequency bands or pixel intensities, allowing the model to focus on specific frequency ranges or image features. In time-series data analysis, the frequency axis cutting approach can be applied by partitioning the temporal sequence into distinct frequency components, enabling efficient feature extraction in tasks like signal processing or financial forecasting. Moreover, in sensor data processing, the technique can be extended to segregate sensor readings based on frequency characteristics, facilitating lightweight deep learning models for sensor fusion or anomaly detection applications. By generalizing the frequency axis cutting technique, the DSSDN architecture can be leveraged across a wide range of domains for efficient and effective feature extraction.