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Noise-mapping is an effective sound visualization tool for the identification of urban noise hotspots, which is crucial to taking targeted measures to tackle environmental noise pollution. This paper develops a high-resolution wideband acoustic source mapping methodology using a portable microphone array, where the joint localization and power spectrum estimation of individual sources sparsely distributed over a large region are achieved by tomographic imaging with the multi-frequency delay-and-sum beamforming power outputs from multiple array positions. Exploiting the fact that a wideband source has a common spatial signal-support across the frequency spectrum, two-dimensional tomographic maps are produced by applying compressive sensing techniques including group least absolute shrinkage selection operator formulation and sparse Bayesian learning to promote group sparsity over multiple frequency bands. The high-resolution mapping is demonstrated with experimental data recorded with a microphone array mounted atop an electric vehicle driven along a road while playing audio clips from a loudspeaker positioned within the adjacent open field. [ABSTRACT FROM AUTHOR]

Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples. [ABSTRACT FROM AUTHOR]

Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)]. [ABSTRACT FROM AUTHOR]

Environmental noise is a risk factor for human physical and mental health, demanding an efficient large-scale noise-monitoring scheme. The current technology, however, involves extensive sound pressure level (SPL) measurements at a dense grid of locations, making it impractical on a city-wide scale. This paper presents an alternative approach using a microphone array mounted on a moving vehicle to generate two-dimensional acoustic tomographic maps that yield the locations and SPLs of the noise-sources sparsely distributed in the neighborhood traveled by the vehicle. The far-field frequency-domain delay-and-sum beamforming output power values computed at multiple locations as the vehicle drives by are used as tomographic measurements. The proposed method is tested with acoustic data collected by driving an electric vehicle with a rooftop-mounted microphone array along a straight road next to a large open field, on which various pre-recorded noise-sources were produced by a loudspeaker at different locations. The accuracy of the tomographic imaging results demonstrates the promise of this approach for rapid, low-cost environmental noise-monitoring. [ABSTRACT FROM AUTHOR]

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Male frogs and toads call in dense choruses to attract females. Determining the vocal interactions and spatial distribution of the callers is important for understanding acoustic communication in such assemblies. It has so far proved difficult to simultaneously locate and recover the vocalizations of individual callers. Here a microphone-array technique is developed for blindly locating callers using arrival-time delays at the microphones, estimating their steering-vectors, and recovering the calls with a frequency-domain adaptive beamformer. The technique exploits the time-frequency sparseness of the signal space to recover sources even when there are more sources than sensors. The method is tested with data collected from a natural chorus of Gulf Coast toads (Bufo valliceps) and Northern cricket frogs (Acris crepitans). A spatial map of locations accurate to within a few centimeters is constructed, and the individual call waveforms are recovered for nine individual animals within a 9x9 m². These methods work well in low reverberation when there are no reflectors other than the ground. They will require modifications to incorporate multi-path propagation, particularly for the estimation of time-delays. [ABSTRACT FROM AUTHOR]

Direction finding of more sources than sensors is appealing in situations with small sensor arrays. Potential applications include surveillance, teleconferencing, and auditory scene analysis for hearing aids. A new technique for time-frequency-sparse sources, such as speech and vehicle sounds, uses a coherence test to identify low-rank time-frequency bins. These low-rank bins are processed in one of two ways: (1) narrowband spatial spectrum estimation at each bin followed by summation of directional spectra across time and frequency or (2) clustering low-rank covariance matrices, averaging covariance matrices within clusters, and narrowband spatial spectrum estimation of each cluster. Experimental results with omnidirectional microphones and colocated directional microphones demonstrate the algorithm’s ability to localize 3–5 simultaneous speech sources over 4 s with 2–3 microphones to less than 1 degree of error, and the ability to localize simultaneously two moving military vehicles and small arms gunfire. [ABSTRACT FROM AUTHOR]

Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, and locate and identify prey. Their echolocation signals have been characterized in detail but their communication signals are less well understood despite their widespread use during social interactions. The goal of this study was to develop an automatic classification algorithm for characterizing the communication signals of little brown bats. Sound recordings were made overnight on five individual male bats (housed separately from a large group of captive bats) for 7 nights, using a bat detector and a digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed and classified by visual observation of a call’s temporal pattern and spectral composition. Sounds were later classified using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat’s communication signals: downward frequency-modulated calls, steep frequency-modulated calls, constant frequency calls, broadband noise bursts, and broadband click trains. [ABSTRACT FROM AUTHOR]

For some time, compact acoustic vector sensors (AVSs) capable of sensing particle velocity in three orthogonal directions have been used in underwater acoustic sensing applications. Potential advantages of using AVSs in air include substantial noise reduction with a very small aperture and few channels. For this study, a four-microphone array approximating a small (1 cm3) AVS in air was constructed using three gradient microphones and one omnidirectional microphone. This study evaluates the signal extraction performance of one nonadaptive and four adaptive beamforming algorithms. Test signals, consisting of two to five speech sources, were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio (SNR) of the output. For a three-microphone array, robust and nonrobust versions of a frequency-domain minimum-variance (FMV) distortionless-response beamformer produced SNR improvements of 11 to 14 dB, and a generalized sidelobe canceller (GSC) produced improvements of 5.5 to 8.5 dB. In comparison, a two-microphone omnidirectional array with a spacing of 15 cm yielded slightly lower SNR improvements for similar multi-interferer speech signals. [ABSTRACT FROM AUTHOR]

Extraction of a target sound source amidst multiple interfering sound sources is difficult when there are fewer sensors than sources, as is the case for human listeners in the classic cocktail-party situation. This study compares the signal extraction performance of five algorithms using recordings of speech sources made with three different two-microphone arrays in three rooms of varying reverberation time. Test signals, consisting of two to five speech sources, were constructed for each room and array. The signals were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio of the output. A frequency-domain minimum-variance distortionless-response beamformer outperformed the time-domain based Frost beamformer and generalized sidelobe canceler for all tests with two or more interfering sound sources, and performed comparably or better than the time-domain algorithms for tests with one interfering sound source. The frequency-domain minimum-variance algorithm offered performance comparable to that of the Peissig-Kollmeier binaural frequency-domain algorithm, but with much less distortion of the target signal. Comparisons were also made to a simple beamformer. In addition, computer simulations illustrate that, when processing speech signals, the chosen implementation of the frequency-domain minimum-variance technique adapts more quickly and accurately than time-domain techniques. [ABSTRACT FROM AUTHOR]

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. Many state-of-the-art audio signal processing algorithms, for example in hearing-aids and telephony, are expected to have the ability to characterize the listening environment, and turn on an appropriate processing strategy accordingly. Thus, a method for characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, a method for estimating RT without prior knowledge of sound sources or room geometry is presented. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time-constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values. [ABSTRACT FROM AUTHOR]

This paper describes algorithms for signal extraction for use as a front-end of telecommunication devices, speech recognition systems, as well as hearing aids that operate in noisy environments. The development was based on some independent, hypothesized theories of the computational mechanics of biological systems in which directional hearing is enabled mainly by binaural processing of interaural directional cues. Our system uses two microphones as input devices and a signal processing method based on the two input channels. The signal processing procedure comprises two major stages: (i) source localization, and (ii) cancellation of noise sources based on knowledge of the locations of all sound sources. The source localization, detailed in our previous paper [Liu et al., J. Acoust. Soc. Am. 108, 1888 (2000)], was based on a well-recognized biological architecture comprising a dual delay-line and a coincidence detection mechanism. This paper focuses on description of the noise cancellation stage. We designed a simple subtraction method which, when strategically employed over the dual delay-line structure in the broadband manner, can effectively cancel multiple interfering sound sources and consequently enhance the desired signal. We obtained an 8-10 dB enhancement for the desired speech in the situations of four talkers in the anechoic acoustic test (or 7-10 dB enhancement in the situations of six talkers in the computer simulation) when all the sounds were equally intense and temporally aligned. © 2001 Acoustical Society of America. [ABSTRACT FROM AUTHOR]

Control rooms designers typically measure and specify rooms according to their physical structure and acoustic properties. They are unable, however, to measure or predict how well the room will support the subjective qualities of stereo imagery produced over loudspeakers. As the quality and salience of stereo imagery improve through the use of more sophisticated recording and processing techniques, control room requirements become more stringent. Beyond speaker placement, there are three primary factors that influence the perception of stereo images: time-energy-frequency characteristics of the speakers, spatio-temporal distribution of early reflections, and the inclusion of acoustic diffraction. These are easily measured through the use of time delay spectrometry (TDS), but at present an adequate model for predicting subjective response from these physical measurements is lacking. Ensuring the perception of optimal stereo imagery requires the application of standardized subjective evaluation techniques. Currently under development at Northwestern Computer Music (NCM) is an evaluation technique using the Listening Environment Diagnostic Recording (LEDR™), which enables the immediate assessment of changes in stereo imagery that result from progressive changes in control room acoustical treatment. Field tests indicate that LEDR™ is valuable in the design and modification of control rooms for optimizing stereo imagery. [ABSTRACT FROM AUTHOR]

Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)].

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.