Toward end-to-end interpretable convolutional neural networks for waveform signals (2405.01815v1)

Abstract: This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models, presenting advancements in efficiency and explainability. By benchmarking experiments on three standard speech emotion recognition datasets with five-fold cross-validation, our framework outperforms Mel spectrogram features by up to seven percent. It can potentially replace the Mel-Frequency Cepstral Coefficients (MFCC) while remaining lightweight. Furthermore, we demonstrate the efficiency and interpretability of the front-end layer using the PhysioNet Heart Sound Database, illustrating its ability to handle and capture intricate long waveform patterns. Our contributions offer a portable solution for building efficient and interpretable models for raw waveform data.

• The paper introduces IConNet, which integrates window functions into CNNs to directly process waveform signals, improving feature extraction and model transparency.
• The approach reduces spectral leakage and adapts convolution kernels using generalized cosine windows, achieving up to 7% accuracy gains in speech emotion recognition.
• The framework simplifies preprocessing for heart sound detection and provides visual insights into filter decisions, enhancing reliability in clinical applications.

Exploring the Depths of IConNet: Revolutionizing Waveform Signal Processing with CNNs

Introduction to IConNet

In the field of signal processing, and particularly when dealing with audio data, extracting pertinent features directly from waveform signals can often present a significant challenge. The introduction of Convolutional Neural Networks (CNNs) tailored for this task brought about substantial progress, yet efforts to improve the interpretability and efficiency of such models persist. The recent development of IConNet, a specialized CNN framework, represents an innovative approach that embraces both end-to-end processing and enhanced understandability of its inner workings, specifically for waveform signals.

The IConNet Architecture

IConNet stands out by integrating window functions directly into the convolutional layer, fundamentally altering how the model interacts with incoming waveform data. Here's a breakdown of its unique architectural elements:

• Front-end block configuration: The initial layers of IConNet utilize window functions to shape and filter the input signal, which greatly aids in minimizing spectral leakage—a common issue in signal processing that can lead to significant errors in feature extraction.
• Utilization of Generalized Cosine Windows: This choice enables the convolutional kernels themselves to adapt their shape based on the input signal, promoting a more efficient learning process as the model tailors itself to the specifics of the data it processes.
• Enhanced downsampling and normalization: Following the convolutional layers, the architecture employs a downsampling step which reduces dimensionality and computational load while preserving essential features. Additionally, a normalization step ensures that the model remains stable and that its outputs are consistent across different inputs.

This architecture not only boosts the model's performance by focusing on critical frequencies and optimizing signal representation but also offers greater insight into what features are deemed important by the model during training.

Groundbreaking Models and Performance Insights

In testing IConNet across different setups and comparing its performance with traditional methods like Mel spectrograms and MFCCs (Mel-Frequency Cepstral Coefficients), IConNet demonstrated an impressive ability to outperform these established benchmarks, particularly in the domains of speech emotion recognition and abnormal heart sound detection.

• Speech Emotion Recognition: IConNet variants consistently surpassed traditional Mel and MFCC features in recognizing emotional cues from speech, showing up to a 7% improvement in accuracy. Moreover, models using learnable window functions generally outperformed those adjusting only frequency bands, indicating a significant advantage in adaptively shaping windows directly through the learning process.
• Heart Sound Detection: In detecting abnormalities in heart sounds, the proposed model also exhibited superior accuracy and F1-scores compared to the baselines, even bypassing a sophisticated MFCC-based model with additional preprocessing steps. The IConNet framework effectively negated the need for extensive preprocessing, streamlining the process while enhancing model interpretability and performance.

Visualizing and Understanding Model Decisions

One of the standout features of IConNet is its interpretability—an essential factor in medical applications where understanding model rationale can be as critical as the diagnosis itself. The architecture allows for visualization of the convolutional filters, providing insights into what features are prioritized during training and why certain decisions are made, a level of transparency crucial for trust and reliability in clinical settings.

Future Directions and Considerations

While the results are promising, the exploration into fine-tuned configurations and further empirical validation remains open. Future research might investigate the integration of domain-specific knowledge into the window shaping process or explore the scalability of IConNet across other types of waveform data beyond audio, potentially broadening its applicability in other critical fields such as seismic or radio signal processing.

Conclusion

IConNet represents a significant step forward in the design of neural networks for waveform signal processing, providing not only enhanced performance but also greater transparency in its operations. Its success in tackling complex audio classification tasks suggests a bright future, potentially setting a new standard in how we approach and implement CNNs for end-to-end signal processing.