Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1908.07324

A Microphone Array and Voice Algorithm based Smart Hearing Aid

Bharath Sudharsan·Manigandan Chockalingam
View original source ↗Read source document ↗Export metadata ↓

Abstract

Approximately 6.2% of the world's population (466 million people) suffer from disabling hearing impairment [1]. Hearing impairment impacts negatively on one's education, financial success [2][3], cognitive development in childhood [4], including increased risk of dementia in older adulthood [5]. Lack of or reduced social interaction due to hearing impairment affects creating or maintaining healthy relationships at home, school and work [5]. Hence, hearing impairment genuinely affects the overall quality of life and wellbeing. The cocktail party effect, which is a healthy hearing individual's ability to understand one voice in a cacophony of other voices or sounds, is an important ability lacking in people with hearing impairment. This inability results in difficulties with simple daily activities such as partaking in group discussions or conversing in noisy restaurants [6]. This smart hearing aid aims to provide much-needed assistance with understanding speech in noisy environments. For example, if a person wants to partake in a group discussion, he/she needs to place the microphone array based unit on a flat surface in front of him/her, such as a table. When conversations take place, the microphone array will capture and process sound from all directions, intelligently prioritise and provide the lead speaker's voice by suppressing unwanted noises, including speeches of other people. This device selects and alternates voices between speakers automatically using voice algorithms. Additionally, the user has the option of further fine-tuning the acoustic parameters as needed through a smartphone interface. This paper describes the development and functions of this new Smart Hearing Aid.

Explore related subjects

cs.SDeess.AS

Keep this discovery

BibTeXRIS

Bharath Sudharsan, Manigandan Chockalingam. 2019-08-20. A Microphone Array and Voice Algorithm based Smart Hearing Aid. https://doi.org/10.5120/ijca2019919295

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↗