An efficient light-weighted signal reconstruction method consists of Fast Fourier Transform and Convolutional-based Autoencoder (2501.01650v1)

Abstract: The main theme of this paper is to reconstruct audio signal from interrupted measurements. We present a light-weighted model only consisting discrete Fourier transform and Convolutional-based Autoencoder model (ConvAE), called the FFT-ConvAE model for the Helsinki Speech Challenge 2024. The FFT-ConvAE model is light-weighted (in terms of real-time factor) and efficient (in terms of character error rate), which was verified by the organizers. Furthermore, the FFT-ConvAE is a general-purpose model capable of handling all tasks with a unified configuration.

• The paper proposes the FFT-ConvAE model, which combines Fast Fourier Transform preprocessing with a Convolutional Autoencoder for light-weight audio signal reconstruction.
• The FFT-ConvAE model achieves a real-time factor below 1 and low character error rates across various audio tasks, demonstrating its efficiency and performance.
• This efficient method has implications for real-time audio processing in resource-constrained environments like mobile and edge computing devices.

Overview of FFT-ConvAE Method for Audio Signal Reconstruction

The paper presents a novel approach for reconstructing audio signals from interrupted measurements using a method that combines Fast Fourier Transform (FFT) and a Convolutional-based Autoencoder (ConvAE), dubbed the FFT-ConvAE model. The primary motivation for this research is participation in the Helsinki Speech Challenge 2024, with a focus on creating a light-weight and efficient model with low character error rates and a unified approach to various audio processing tasks.

Methodology

The proposed FFT-ConvAE model leverages two main components:

1. Fast Fourier Transform (FFT): This preprocesses audio signals to transform them into the frequency domain. Leveraging FFT aids in efficiently handling the data by focusing on frequency components, which are often less sensitive to noise than time-domain data.
2. Convolutional-based Autoencoder (ConvAE): This model is designed to learn a compressed representation of the transformed audio signals. The autoencoder architecture, enhanced by convolutional layers, helps to capture underlying features that are essential for signal reconstruction. The network structure consists of encoding and decoding phases which utilize linear activation functions, keeping the model lightweight and avoiding over-parameterization.

A critical innovation is the approximator applied to the Fourier coefficients of the audio signals, where a ratio-based adjustment is made to minimize noise effects. This approach is computationally efficient and improves the model’s ability to generalize across different tasks without incurring significant computational overhead.

Results and Performance

The FFT-ConvAE model was rigorously tested across multiple tasks designed for the Helsinki Speech Challenge, categorized into filtering experiments, reverb experiments, and combinations of both. The real-time factor, a key performance indicator, demonstrates the model’s lightweight nature, consistently staying below 1 across different datasets.

• The model achieves a real-time factor significantly lower than the threshold of 3, which is a constraint in the challenge.
• The character error rate (CER), an important metric for speech quality, remains low, especially in tasks focusing on filtering experiments, showcasing the model’s effectiveness in handling those with less complex features.
• Notably, the model performs better in environments where high-frequency information can significantly distinguish between background noise and the speech signal.

Implications and Future Directions

The research discusses the broader implications for AI applications in audio signal processing, particularly in environments demanding real-time performance with limited computational resources. The FFT-ConvAE model opens up possibilities for implementing speech reconstruction algorithms in mobile devices and other edge computing scenarios, where resources are often constrained.

Theoretically, the method provides insights into handling inverse problems within audio processing, demonstrating that a thoughtfully designed preprocessing step (FFT in this case) can significantly enhance the capabilities of neural networks by focusing on stable feature areas.

Future research could explore more complex architectures or integration with other signal processing techniques to address the challenges observed in tasks involving reverb. Moreover, improving the model’s sensitivity to phase information, which proved problematic in certain tasks, could be achieved by more advanced data preprocessing or modifications in the learning strategy. The exploration of different transforms beyond FFT, such as wavelet transforms, may also provide fruitful avenues for further enhancement.