Frequency domain binaural model based on interaural phase and level differences

Abstract

Abstract:We can communicate with others in a noisy environment. This phenomenon is known as a "Cocktail Party Effect" and is one of the most important binaural functions. This paper addresses a frequency domain binaural model that plays the role of a binaural function based on an interaural phase and level difference. The proposed model is evaluated not only as a front-end of the speech recognition system, but also as a speech enhancer. According to the evaluation, when the direction of arrival of the target signal and noise differs by 10 , recognition rates improve in comparison with the previous time domain binaural model (TDBM) in any cases. Furthermore, recognition rates show more than 90% when the signal to noise ratio (SNR) is higher than approximately 5 dB. On the other hand, SNR and coherence of the frequency domain binaural model, which is obtained for an evaluation of the speech enhancer, show superior results over the TDBM.

Binaural wind noise cancellation for hearing aids based on head-related transfer function cues

“…Thus, DOA estimation is possible using the IPD and ILD of the observed binaural signals. These characteristics are stored in a database in the proposed system's memory [5] for wind noise detection. The database entries for each frequency band, DB ðk; Þ and $ DB ðk; Þ, are determined from the IPDs and ILDs, where is the azimuth angle and k is the frequency bin index.…”

Quantitative evaluation of segregated signal with frequency domain binaural model

“…Among these methods, frequency domain binaural model (FDBM) [3] has the advantages of low computational load and effective howling cancellation [4].…”

Azimuthal and elevation localization of two sound sources using interaural phase and level differences

“…A binaural model using two sensors that addressing a part of the cocktail party effect is one possibility. In the context of binaural models, the frequency domain binaural model (FDBM) was proposed by Nakashima et al [4]. This is a model approximating the human auditory function.…”