design filter banks

To address the permutation ambiguity problem inherent in frequency-domain blind source separation (BSS) methods—specifically Independent Vector Analysis (IVA) and Independent Low-Rank Matrix Analysis (ILRMA)—this paper proposes a Subband Splitting (SS) framework. The spectrogram is partitioned into overlapping subbands; BSS is applied sequentially to each subband, with the separation result from the preceding subband used to initialize the current one, thereby achieving inter-subband permutation alignment. This work is the first to jointly integrate subband decomposition with cross-band initialization, enabling near-ideal permutation performance without supervision or auxiliary modeling. Based on this framework, two novel algorithms—SS-IVA and SS-ILRMA—are developed. They achieve substantial improvements in separation quality and convergence speed with negligible additional computational cost. Experiments demonstrate that SS-ILRMA matches the performance of frequency-domain ICA equipped with an ideal permutation solver, while converging significantly faster than conventional IVA and ILRMA.

This work investigates the design of pooling-free scattering networks employing fixed monomial nonlinearities to maximize separability for data with low intrinsic dimensionality. By integrating frame theory, geometric measure theory, and moment analysis, the study provides the first geometric characterization of a scattering network’s separation capacity, establishing theoretical bounds for feature extractors operating on low-dimensional rectifiable data. The core contribution consists of two practical design principles: the network’s filters must span a sufficiently broad frequency range, and the frame formed by these filters—when coupled with the data’s geometric structure through a coupling matrix—must exhibit a well-conditioned condition number. These criteria jointly ensure significantly enhanced separation performance, offering concrete guidance for the construction of effective scattering architectures tailored to geometrically structured low-dimensional data.

In strong interference scenarios, modal components and interference become coupled in time-frequency spectrograms of multicomponent signals, severely degrading ridge detection accuracy. Method: This paper proposes a spectrogram decoupling framework that pioneers the integration of texture–geometry decomposition into time-frequency analysis. It establishes a dual-path architecture comprising a variational optimization model and a U-Net-based supervised learning network to separate intrinsic mode components from interference components in spectrograms. Furthermore, it introduces an interference-estimation-driven local adaptive window-length selection criterion, overcoming the limitations of fixed window lengths. Results: Evaluated on a synthetic multi-interference spectrogram dataset, the method significantly improves ridge localization accuracy (average gain of +27%), demonstrating both high precision and strong robustness. The two complementary pathways synergistically enhance overall performance.

To address the challenge of detecting 6-kHz narrowband transient components in real-time signals, this paper proposes an FPGA-optimized wavelet spectral analysis method. Unlike conventional FFT-based approaches, which suffer from inherent trade-offs between time-frequency resolution and latency, our method fully hardware-implements the Daubechies wavelet transform—including fixed-point arithmetic design, pipelined convolution, and on-chip RAM caching—on a Xilinx Artix-7 FPGA. The architecture achieves both high precision and ultra-low latency: under a 250 MS/s input throughput, detection latency is below 5 μs, with total system power consumption under 1.2 W. This work overcomes the real-time detection bottleneck for narrowband transients on resource-constrained embedded platforms and establishes a reusable hardware acceleration paradigm for edge intelligence in high-frequency dynamic signal sensing.

To address the challenge of deploying computationally expensive music source separation models on resource-constrained devices, this paper proposes a lightweight band-split U-Net architecture. The method introduces band-split convolution and a dual-path feature fusion mechanism within the U-Net encoder-decoder framework, enabling frequency-band-adaptive modeling and efficient cross-path information interaction. Despite its significantly reduced parameter count—13× fewer than state-of-the-art large models—the proposed architecture achieves competitive source separation performance, matching top-performing models in Signal-to-Distortion Ratio (SDR) on MUSDB-HQ. Moreover, it demonstrates strong generalization and scalability on the extended MoisesDB dataset. This work substantially raises the performance ceiling for lightweight source separation models and provides a viable solution for real-time, on-device music separation.

This work addresses the longstanding trade-off between performance and latency in large finite impulse response (FIR) filters commonly used in image, video, and audio processing. The authors propose a unified design language that abstracts multirate filtering, recursive filtering, and filter decomposition into composable primitives. By combining program-space search with gradient-based optimization of continuous parameters, the framework automatically synthesizes Pareto-optimal approximate filtering algorithms. This approach enables, for the first time, the systematic integration of diverse fast filtering techniques and fully automated code generation, producing vectorized and parallelized C++ implementations. Evaluated across multiple mainstream image and audio tasks, the generated filters consistently outperform existing methods in both speed and accuracy.

This work addresses the challenge that conventional graph signal processing methods struggle to effectively model node data in heterogeneous networks due to disparities in dimensionality, modality, and geometric structure. To overcome this limitation, the authors propose a unified framework termed Layered Signal Processing (SSP), which characterizes heterogeneous local signal spaces through network layers and the linear mappings between them, thereby generalizing fundamental operations such as spectral analysis, filtering, and sampling. Key contributions include the first formal definition of the Layered Fourier Transform (SFT), whose frequency basis is constructed from topological and restriction mappings; the introduction of representation layers that accommodate diverse bases, dictionaries, or embeddings while preserving spectral properties; and the design of polynomial layered filters along with a joint node-component sampling strategy. Experiments on synthetic, motion capture, and financial datasets demonstrate significant performance gains over classical baselines, and the framework establishes conditions for perfect reconstruction of bandlimited signals.

This work addresses the lack of a unified and reproducible experimental framework in music source separation research, which has hindered systematic comparison and rapid iteration. To this end, the authors propose MSST, an open-source framework featuring a YAML-driven architecture that integrates, for the first time, practical techniques such as sliding-window inference, test-time augmentation, model ensembling, and LoRA-based fine-tuning within a single pipeline. MSST supports diverse models, data augmentation strategies, loss functions, and evaluation metrics. Through comprehensive ablation studies, the authors demonstrate the effectiveness of these integrated components, showing consistent improvements in separation performance while substantially lowering the barrier to reproduction and development.

This work addresses the lack of effective online experimental platforms in signal processing education and engineering talent development. To bridge this gap, the authors developed and have continuously refined J-DSP, a web-based simulation environment that pioneered the migration of the original Java-based DSP toolkit to an HTML5 architecture, enabling cross-platform— including mobile—accessibility. The platform integrates advanced topics such as digital filter design, FFT-based spectral analysis, machine learning for signal classification, and quantum Fourier transform. Having operated reliably for 25 years, J-DSP has been widely adopted in university courses and National Science Foundation–funded programs, including REU, IRES, and RET initiatives, significantly advancing the modernization of signal processing pedagogy and fostering STEM workforce development.

This work addresses the limitations of existing filter design methods, which often lack flexibility and physical consistency, particularly in simultaneously enforcing spatial symmetry and diverse frequency-domain constraints. To overcome these challenges, the authors propose a general constrained optimization framework implemented in a Python library called pyDOF. This tool enables users to specify custom transfer function constraints and automatically synthesizes discrete forward and inverse filters that satisfy complex design requirements. The approach innovatively integrates adaptive template selection, van Cittert iterative deconvolution with controllable reconstruction order, and a highly configurable constraint mechanism, thereby transcending conventional design limitations. The framework efficiently generates filter coefficients for low-pass, high-pass, and multi-bandpass/bandstop configurations, demonstrating broad applicability in computational fluid dynamics and generalized signal processing tasks.