Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1405.4843

Trends and Perspectives for Signal Processing in Consumer Audio

Joshua Atkins·Daniele Giacobello
View original source ↗Read source document ↗Export metadata ↓

Abstract

The trend in media consumption towards streaming and portability offers new challenges and opportunities for signal processing in audio and acoustics. The most significant embodiment of this trend is that most music consumption now happens on-the-go which has recently led to an explosion in headphone sales and small portable speakers. In particular, premium headphones offer a gateway for a younger generation to experience high quality sound. Additionally, through technologies incorporating head-related transfer functions headphones can also offer unique new experiences in gaming, augmented reality, and surround sound listening. Home audio has also seen a transition to smaller sound systems in the form of sound bars. This speaker configuration offers many exciting challenges for surround sound reproduction which has traditionally used five speakers surrounding the listener. Furthermore, modern home entertainment systems offer more than just content delivery; users now expect wireless and connected smart devices with video conferencing, gaming, and other interactive capabilities. With this comes challenges for voice interaction at a distance and in demanding conditions, e.g., during content playback, and opportunities for new smart interactive experiences based on awareness of environment and user biometrics.

Explore related subjects

cs.SDcs.MM

Keep this discovery

BibTeXRIS

Joshua Atkins, Daniele Giacobello. 2014-05-19. Trends and Perspectives for Signal Processing in Consumer Audio. https://arxiv.org/abs/1405.4843

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

KEEP EXPLORING

Related papers

arXiv2026

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD↗
arXiv2026

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD↗