IPDnet2: an efficient and improved inter-channel phase difference estimation network for sound source localization

Yabo Wang; Bing Yang; Xiaofei Li

Home/Archive/arXiv

arXiv · 2509.21900

IPDnet2: an efficient and improved inter-channel phase difference estimation network for sound source localization

Yabo Wang·Bing Yang·Xiaofei Li

View original source ↗Read source document ↗Export metadata ↓

Abstract

IPDnet is our recently proposed real-time sound source localization network. It employs alternating full-band and narrow-band (B)LSTMs to learn the full-band correlation and narrow-band extraction of DP-IPD, respectively, which achieves superior performance. However, processing narrow-band independently incurs high computational complexity and the limited scalability of LSTM layers constrains the localization accuracy. In this work, we extend IPDnet to IPDnet2, improving both localization accuracy and efficiency. IPDnet2 adapts the oSpatialNet as the backbone to enhance spatial cues extraction and provide superior scalability. Additionally, a simple yet effective frequency-time pooling mechanism is proposed to compress frequency and time resolutions and thus reduce computational cost, and meanwhile not losing localization capability. Experimental results show that IPDnet2 achieves comparable localization performance with IPDnet while only requiring less than 2\% of its computation cost. Moreover, the proposed network achieves state-of-the-art SSL performance by scaling up the model size while still maintaining relatively low complexity.

Explore related subjects

eess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yabo Wang, Bing Yang, Xiaofei Li. 2025-09-26. IPDnet2: an efficient and improved inter-channel phase difference estimation network for sound source localization. https://arxiv.org/abs/2509.21900

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Recovering the Zipfian Distribution in Unsupervised Term Discovery

Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate types. True lexicons follow a Zipfian distribution, yet the dominant centre-based clustering approach -- K-means -- produces a more uniform distribution due to an inductive bias toward spherical clusters. In this paper we revisit graph-based clustering as a bottom-up alternative, where segment embeddings are connected by pairwise similarity and partitioned using the Leiden algorithm. We show that graph clustering substantially outperforms centre-based approaches (K-means, GMM, BIRCH) in both word- and syllable-level lexicon discovery across three languages, producing more Zipf-like distributions. Another bottom-up approach, agglomerative clustering with average linkage, also performs well, although it is computationally less efficient and allows for less control over the resulting distribution. Our work calls into question the dominance of centre-based clustering for term discovery, and promotes graph clustering as an attractive alternative.

eess.AS↗

arXiv2026

Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation

Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can degrade the original S2T performance, while attaching a downstream talker reintroduces a serial text-to-speech bottleneck. We present PRIME-Speech, a frozen-backbone S2S conversion framework that trains only speech-generation modules. PRIME-Speech synchronizes a causal audio post-decoder with intermediate hidden states of the frozen backbone, so codec tokens are generated from the model's evolving reasoning trajectory rather than from completed text chunks. The post-decoder uses mixed hidden-state, text, and audio-history conditioning, and a training-time packing strategy with turn-level audio KV-cache and position reset stabilizes multi-turn spoken interaction without additional multi-turn S2S training data. Multi-token prediction further reduces the effective codec prediction rate and improves first-audio latency without modifying the reasoning path. Across speech translation, spoken QA, speech understanding, and multi-turn dialogue, PRIME-Speech preserves the S2T behavior of the frozen backbone while producing accurate, low-WER spoken responses.

eess.AS↗

arXiv2026

Brain2Speech-Net: Fast and Intelligible Brain-to-Speech Synthesis Without Text Decoding

The loss of speech limits communication for individuals with paralysis. Direct neural-to-speech synthesis is challenging due to the limited availability of neural data for training speech brain-computer interfaces. Most existing systems rely on cascaded neural-to-text-to-speech pipelines, which increase inference latency and propagate errors across stages. We present Brain2Speech-Net, a single-stage neural-to-speech generation framework without intermediate text decoding. We use a differentiable phoneme bottleneck and a deep-HMM alignment mechanism to map long neural recordings into the latent space of a text-to-speech (TTS) model, enabling high-quality speech synthesis. Brain2Speech-Net is the only system in our comparison that produces intelligible speech while generating faster than real time.

eess.AS↗