Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure

Effects of Hearing Aid Amplification on Robust Neural Coding of Speech

Hearing aids are able to restore some hearing abilities for people with auditory impairments, but background noise remains a significant problem. Unfortunately, we know very little about how speech is encoded in the auditory system, particularly in impaired systems with prosthetic amplifiers. There is growing evidence that relative timing in the neural signals (known as spatiotemporal coding) is important for speech perception, but there is little research that relates spatiotemporal coding and hearing aid amplification. This research uses a combination of computational modeling and physiological experiments to characterize how hearing aids affect vowel coding in noise at the level of the auditory nerve. The results indicate that sensorineural hearing impairment degrades the temporal cues transmitted from the ear to the brain. Two hearing aid strategies (linear gain and wide dynamic-range compression) were used to amplify the acoustic signal. Although appropriate gain was shown to improve temporal coding for individual auditory nerve fibers, neither strategy improved spatiotemporal cues. Previous work has attempted to correct the relative timing by adding frequency-dependent delays to the acoustic signal (e.g., within a hearing aid). We show that, although this strategy can affect the timing of auditory nerve responses, it is unlikely to improve the relative timing as intended. We have shown that existing hearing aid technologies do not improve some of the neural cues that we think are important for perception, but it is important to understand these limitations. Our hope is that this knowledge can be used to develop new technologies to improve auditory perception in difficult acoustic environments

Speech and crosstalk detection in multichannel audio

The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation

Electrophysiologic assessment of (central) auditory processing disorder in children with non-syndromic cleft lip and/or palate

Session 5aPP - Psychological and Physiological Acoustics: Auditory Function, Mechanisms, and Models (Poster Session)Cleft of the lip and/or palate is a common congenital craniofacial malformation worldwide, particularly non-syndromic cleft lip and/or palate (NSCL/P). Though middle ear deficits in this population have been universally noted in numerous studies, other auditory problems including inner ear deficits or cortical dysfunction are rarely reported. A higher prevalence of educational problems has been noted in children with NSCL/P compared to craniofacially normal children. These high level cognitive difficulties cannot be entirely attributed to peripheral hearing loss. Recently it has been suggested that children with NSCLP may be more prone to abnormalities in the auditory cortex. The aim of the present study was to investigate whether school age children with (NSCL/P) have a higher prevalence of indications of (central) auditory processing disorder [(C)APD] compared to normal age matched controls when assessed using auditory event-related potential (ERP) techniques. School children (6 to 15 years) with NSCL/P and normal controls with matched age and gender were recruited. Auditory ERP recordings included auditory brainstem response and late event-related potentials, including the P1-N1-P2 complex and P300 waveforms. Initial findings from the present study are presented and their implications for further research in this area —and clinical intervention—are outlined. © 2012 Acoustical Society of Americapublished_or_final_versio