The recent developments in the field of acoustic signal processing and audio technology have been marked by significant advancements in both theoretical approaches and practical applications. A notable trend is the integration of deep learning techniques with traditional signal processing theories, leading to more robust and efficient systems. Innovations such as attention mechanisms and novel filtering approaches are being employed to enhance the performance of acoustic echo cancellation and active noise control systems, addressing longstanding challenges like double-talk interference and output saturation. Furthermore, the field is witnessing a shift towards more sophisticated methods for speaker direction of arrival estimation and acoustic speed estimation, leveraging advanced algorithms and novel sensing paradigms to improve accuracy and reliability in complex environments. On the audio generation front, the adaptation of transformer architectures for neural vocoders represents a leap forward, enabling real-time, high-quality audio synthesis with reduced computational demands.

Noteworthy Papers

• Attention-Enhanced Short-Time Wiener Solution for Acoustic Echo Cancellation: Introduces an innovative approach combining attention mechanisms with traditional filter theory, significantly improving echo cancellation performance.
• Preventing output saturation in active noise control: Proposes a modified Kalman filter with output constraints, effectively addressing the issue of output saturation in high noise environments.
• Robust Target Speaker Direction of Arrival Estimation: Presents a real-time DOA estimation system that excels in multi-speaker scenarios, setting new benchmarks for accuracy.
• ASE: Practical Acoustic Speed Estimation Beyond Doppler via Sound Diffusion Field: Develops a novel system for accurate speed estimation using a single microphone, overcoming limitations of traditional Doppler-based methods.
• RingFormer: A Neural Vocoder with Ring Attention and Convolution-Augmented Transformer: Introduces a neural vocoder that combines ring attention with a Conformer architecture, achieving state-of-the-art performance in real-time audio generation.