Mobile Recording Device Recognition Based Cross-Scale and Multi-Level Representation Learning

TL;DR

This paper presents a multi-scale, multi-level deep learning approach for mobile recording device recognition, combining convolutional, LSTM, and transformer models to achieve high accuracy and transferability.

Contribution

The paper introduces a novel multi-level, cross-scale feature learning framework integrating ConvLSTM, BiLSTM, and transformer encoders for improved device recognition.

Findings

Achieved 99.6% accuracy on CCNU_Mobile dataset.

Improved recognition accuracy by 2-12% over baseline.

Demonstrated 87.9% transferability on a new dataset.

Abstract

This paper introduces a modeling approach that employs multi-level global processing, encompassing both short-term frame-level and long-term sample-level feature scales. In the initial stage of shallow feature extraction, various scales are employed to extract multi-level features, including Mel-Frequency Cepstral Coefficients (MFCC) and pre-Fbank log energy spectrum. The construction of the identification network model involves considering the input two-dimensional temporal features from both frame and sample levels. Specifically, the model initially employs one-dimensional convolution-based Convolutional Long Short-Term Memory (ConvLSTM) to fuse spatiotemporal information and extract short-term frame-level features. Subsequently, bidirectional long Short-Term Memory (BiLSTM) is utilized to learn long-term sample-level sequential representations. The transformer encoder then performs cross-scale, multi-level processing on global frame-level and sample-level features, facilitating deep feature representation and fusion at both levels. Finally, recognition results are obtained through Softmax. Our method achieves an impressive 99.6% recognition accuracy on the CCNU_Mobile dataset, exhibiting a notable improvement of 2% to 12% compared to the baseline system. Additionally, we thoroughly investigate the transferability of our model, achieving an 87.9% accuracy in a classification task on a new dataset.