SearcharxivSearch

arXiv · 1511.03440

Combination of binaural and harmonic masking release effects in the detection of a single component in complex tones

Abstract

Both harmonic and binaural signal properties are relevant for auditory processing. To investigate how these cues combine in the auditory system, detection thresholds for an 800-Hz tone masked by a diotic (i.e., identical between the ears) harmonic complex tone were measured in six normal-hearing subjects. The target tone was presented either diotically or with an interaural phase difference (IPD) of 180 degree and in either harmonic or "mistuned" relationship to the diotic masker. Three different maskers were used, a resolved and an unresolved complex tone (fundamental frequency: 160 and 40 Hz) with four components below and above the target frequency and a broadband unresolved complex tone with 12 additional components. The target IPD provided release from masking in most masker conditions, whereas mistuning led to a significant release from masking only in the diotic conditions with the resolved and the narrowband unresolved maskers. A significant effect of mistuning was neither found in the diotic condition with the wideband unresolved masker nor in any of the dichotic conditions. An auditory model with a single analysis frequency band and different binaural processing schemes was employed to predict the data of the unresolved masker conditions. Sensitivity to modulation cues was achieved by including an auditory-motivated modulation filter in the processing pathway. The predictions of the diotic data were in line with the experimental results and literature data in the narrowband condition, but not in the broadband condition, suggesting that across-frequency processing is involved in processing modulation information. The experimental and model results in the dichotic conditions show that the binaural processor cannot exploit modulation information in binaurally unmasked conditions.

Explore related subjects

Keep this discovery

BibTeXRIS

Martin Klein-Hennig, Mathias Dietz, Volker Hohmann. 2015-11-11. Combination of binaural and harmonic masking release effects in the detection of a single component in complex tones. https://arxiv.org/abs/1511.03440

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

KEEP EXPLORING

Related papers

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

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