Many state-of-the-art audio codecs operating in a transform domain provide scalability as a core function by allowing to selectively subtract bits -- usually according to a nonperceptual criterion from the full bit rate data stream. This work presents a different, or even reverse, scalability approach in which a scalable codec can selectively add perceptually significant bits to a low bit rate data stream. The scalable enhancement algorithm presented here operates in the Modified Discrete Cosine Transform domain, which is popular among perceptual audio transform encoders, but its extension on other domains is straightforward. By exploiting the information of an existing low bit rate base layer, the algorithm adds perceptually significant data to the data stream according to a psycho acoustic model, and improves the audio quality at a fraction of the bit rate that would normally be required for the encoding or transmission of the whole audio piece of the same quality. Applications of this can be found in packet retransmission schemes of compressed audio networks and in remote audio enhancement.

Algorithmic and protocol constraints of most low bitrate compression schemes lead to audio signals of low bandwidth and, inevitably, of low perceptual audio quality. Audio bandwidth extension methods address this problem by reconstructing the high frequency spectrum of a degraded signal based on information from the low frequency part. In this work, a novel audio bandwidth extension method is presented in which high frequency reconstruction is achieved through statistical conversion between the low frequency spectrum of the compressed signal and the high frequency part of the uncompressed signal's spectrum. Even though no psychoacoustic model is used, quality evaluation tests show that the proposed method has similar performance to one of the most recent, state-of-the-art, bandwidth extension schemes.

Multichannel audio refers to a widespread technology that enables audio rendering through multiple channels. Audio reproduction with multiple channels has the advantage of recreating the acoustic scene with unprecedented fidelity and of immersing the listener in an acoustic environment that is virtually indistinguishable from reality. However, one of the greatest challenges of multichannel audio is its high storage and transmission requirements especially since accurate rendering through as many possible channels is the main purpose. Audio resynthesis addresses this issue by enabling us to recreate a set of channels at the receiver end by transmitting only one source channel. We propose a new, enhanced, approach on multichannel audio resynthesis which involves a novel residual processing technique and a features alignment method that significantly increase the resynthesis accuracy. Our results show that this latest method leads to higher audio quality and allows for the robust treatment of any type of multichannel signal set.

In this paper, a new method, using the Sammon map, is proposed for visualizing room responses and room response equalization. Specifically, the map provides an important perspective on the formation of clusters of acoustic room responses when cluster validity measures are unable to clearly identify the number of clusters. Additionally, the Sammon map can also be used for displaying the uniformity of the equalized responses at multiple positions. Furthermore, the map does not impose significant computational requirements, when the number of measured responses is reasonably small, and hence can be readily implemented for analyzing multiple position (viz., multiple listener) room equalization performance

Low frequency room acoustic equalization is an extremely challenging problem to solve with realizable digital equalization filters. In this paper we propose a cascaded FIR solution, using a pattern recognition technique, for multiposition room equalization at low frequencies. The cascaded FIR solution allows for a real-time implementation of realizable filters whereas the pattern recognition approach captures the variabilities in room acoustics between different listening positions for multiposition equalization. We compare the results with the popular warping with low order spectral modeling approach, as well as with the multirate approach. The proposed method outperforms both the warping based and the multirate methods as can be evidenced through published data

In this paper, we investigate an alternative to the Gaussian density for modeling signals encountered in audio environments. The observation that sound signals are impulsive in nature, combined with the reverberation effects commonly encountered in audio, motivates the use of the sub-Gaussian density. The new sub-Gaussian statistical model and the separable solution of its maximum likelihood estimator are presented. These are used in an array scenario to demonstrate with both simulations and two different microphone arrays the achievable performance gains. The simulations exhibit the robustness of the sub-Gaussian-based method while the real world experiments reveal a significant performance gain, supporting the claim that the sub-Gaussian model is better suited for sound signals

The location of an acoustical source can be found robustly using the steered response pattern-phase transform (SRP-PHAT) algorithm. However SRP-PHAT can be computationally expensive, requiring a search of a large number of candidate locations. The required spacing between these locations is dependent on sampling rate, microphone array geometry, and source location. In this work, a novel method is presented that calculates a smaller number of test points using an efficient closed-form localization algorithm. This method significantly reduces the number of calculations, while still remaining robust in acoustical environments.

Given a multichannel loudspeaker system, the selection of the crossover frequency between the sub-woofer and the satellite speakers is important for accurate (i.e., distortion-free), reproduction of playback sound. Presently, many home-theater systems have selectable crossover frequencies which are either set by the consumer through listening tests. Alternatively, if the loudspeakers are industry certified, the crossover frequency is set at 80 Hz. A desirable feature is that, besides distortion-free sound output from the individual subwoofer and the satellite speakers, the combined sub-woofer and satellite room acoustical response should exhibit negligible variations around the selected crossover frequency. In this paper, we present an automatic crossover frequency selection algorithm based on an objective measure (viz., the spectral deviation measure) for multichannel home-theater applications that allows better control of the combined subwoofer-satellite response, thereby significantly improving audio quality.

Multichannel audio can immerse a group of listeners in a seamless aural environment. Previously, we proposed a system capable of synthesizing the multiple channels of a virtual multichannel recording from a smaller set of reference recordings. This problem was termed multichannel audio resynthesis and the application was to reduce the excessive transmission requirements of multichannel audio. In this paper, we address the more general problem of multichannel audio synthesis, i.e., how to completely synthesize a multichannel audio recording from a specific stereophonic or monophonic recording, which would significantly enhance the recording's acoustic impression. We approach this problem by extending the model employed for the resynthesis problem. This is accomplished by adapting the resynthesis conversion parameters to the statistical properties of the recording that we wish to enhance. This parameter adaptation is similar to the task adaptation employed in speech recognition, when a specific model is applied to a different environment (speaker, language or channel). One particular approach to this problem is shown here to be quite advantageous toward solving the multichannel audio synthesis problem as well.

Large complex networks have become an inseparable part of modern society. However, very little has been done to develop tools to manage and ensure the security of such networks. Network operators continue to slave over endless daily logs and alerts in a struggle to keep networks operational. Perhaps the most formidable enemy of network operations today is the volume of management data that must be perused. Expensive commercial products attempt to visualize data but with limited utility, as witnessed by the prevailing use of command-line interfaces and homegrown scripts. In addition to data collection tools, operators need to immediately observe and debug the effects of their actions; yet that information is buried deep in the data that pours daily from monitoring equipment. Thus, they need better ways to abstract network events and better, more informative ways to render them.

Traditionally, room response equalization is performed to improve sound quality at a given listener in applications ranging from automobile, home-theater, movie theater, multimedia education in classrooms. However, room responses vary with source and listener positions. Hence, in a multiple listener environment, equalization may be performed through spatial averaging of magnitude responses at locations of interest (e.g., in movie theater equalization). However, the performance of averaging based equalization, at the listeners, may be affected when listener positions change, or due to mismatch between microphone and listener positions (i.e., displacement effects). In this paper, we present a statistical approach to map displacement effects to a magnitude response averaging equalization performance metric. The results indicate that, for the analyzed listener configurations, the zone of equalization depends on, (i) distance of microphones/listeners from a source, (ii) the listener arrangement, and (iii) the source signal spectral composition.

With the advent of high-speed networks such as Internet2, high quality uncompressed transmission of multi-channel audio streams has become possible. For interactive applications, such as a distributed musical performance, minimizing latency is of paramount importance. Given the strict latency requirements, error recovery (via either retransmission or FEC) may not always be successful, and thus concealment is frequently required. In this paper we propose a novel concealment algorithm, based on inter-channel redundancy, for multi-channel, professional quality, uncompressed audio streams, with particular emphasis on an experimental 10.2 audio standard, which provides an immersive experience for the audience and the players in a performance. We also propose a smoothing method based on Bezier curves. We focus on interactive applications; thus we investigate concealment techniques that can be performed in real time. Our algorithms are implemented in a testbed capable of streaming up to 24 uncompressed audio channels with end-to-end latency of less than 6 ms. Our results show that our techniques outperform existing methods. We expect that our protocol will become an important part of a distributed immersive musical performance system currently being developed at our university.

A new quality-scalable high-fidelity multichannel audio compression algorithm based on MPEG-2 advanced audio coding (AAC) is presented. The Karhunen-Loeve transform (KLT) is applied to multichannel audio signals in the preprocessing stage to remove interchannel redundancy. Then, signals in decorrelated channels are compressed by a modified AAC main profile encoder. Finally, a channel transmission control mechanism is used to re-organize the bitstream so that the multichannel audio bitstream has a quality scalable property when it is transmitted over a heterogeneous network. Experimental results show that, compared with AAC, the proposed algorithm achieves a better performance while maintaining a similar computational complexity at the regular bit rate of 64 kbit/sec/ch. When the bitstream is transmitted to narrowband end users at a lower bit rate, packets in some channels can be dropped, and slightly degraded, yet full-channel, audio can still be reconstructed in a reasonable fashion without any additional computational cost.

Selectively canceling signals at specific locations within an acoustical environment with multiple listeners is of significant importance for home theater, automobile, teleconferencing, office, industrial, and other applications. The traditional noise cancellation approach is impractical for such applications because it requires secondary sources to "anti-phase" the primary source, or sensors to be placed on the listeners. In this paper we propose an alternative method for signal cancellation by pre-processing the acoustical signal with a filter known as the eigenfilter , . We examine the theoretical properties of such filters, and investigate the performance (gain) and tradeoff issues such as, spectral distortion. Sensitivity of the performance as a function of the room impulse response duration (reverberation) modeled in the eigenfilter is also investigated.

We propose a novel method of visualizing room equalization at multiple locations using Sammon mapping. The benefits of this method are: (i) simplicity in implementation; (ii) negligible computational requirements to implement the map; (iii) realistic evaluation of equalization performance for our cluster based method in room response equalization at multiple listener locations. Furthermore, the mapping provides an interesting perspective on (i) how the clusters are formed, (ii) how the centroids are determined, and (iii) the result of combining the centroids in a specific manner (to form the final prototype which is then inverted to form the equalization filter). We also compare the results obtained from using the Sammon map to the results obtained by using the spectral deviation measure.

Multichannel audio is attracting rapidly increasing popularity in audio reproduction. In most cases, however, its transmission requirements are extremely demanding compared to the available bandwidth. One possible solution to this problem could be to transmit a reference channel and recreate the remaining channels at the receiving end. Such a method is proposed by taking advantage of spectral conversion techniques that have been successfully applied to speech processing. Applications of the proposed system include transmission of multichannel audio over the current Internet infrastructure and, as an extension of the methods proposed here, remastering of existing monophonic and stereophonic recordings for multichannel rendering.

This paper describes a method for implementing immersive audio rendering filters for single or multiple listeners and loudspeakers. In particular, the paper is focused on the case of single or two listeners with different loudspeaker arrays to determine the weighting vectors for the necessary FIR and IIR filters using the LMS (least-mean-squares) adaptive inverse algorithm. It describes transform-domain LMS adaptive inverse algorithm that is designed for crosstalk cancellation necessary in loudspeaker-based immersive audio rendering. Specifically, each weighting vector of the inverse filter is generated based on psychoacoustic critical band filters and uses the LMS adaptive inverse algorithm to improve performance in the sensitive frequency bands. We also investigate the sensitivity of the listening position under different number of listeners and loudspeakers with various loudspeaker geometries. Performance is measured based on the ipsilateral signal to contralateral signal (crosstalk) ratio that results from the different filter types with and without psychoacoustic critical band filtering.

Immersive audio systems can be used to render virtual sound sources in three-dimensional (3-D) space around a listener. This is achieved by simulating the head-related transfer function (HRTF) amplitude and phase characteristics using digital filters. In this paper, we examine certain key signal processing considerations in spatial sound rendering over headphones and loudspeakers. We address the problem of crosstalk inherent in loudspeaker rendering and examine two methods for implementing crosstalk cancellation and loudspeaker frequency response inversion in real time. We demonstrate that it is possible to achieve crosstalk cancellation of 30 dB using both methods, but one of the two (the Fast RLS Transversal Filter Method) offers a significant advantage in terms of computational efficiency. Our analysis is easily extendable to nonsymmetric listening positions and moving listeners.

In this paper we address the problem of sound source localization in the presence of impulsive noise for application in immersive telepresence and teleconferencing. Traditional Gaussian modeling of noise signals fails when the signals exhibit impulsive behaviour. A new model is used, namely the symmetric /spl alpha/-stable (S/spl alpha/S), which can better account for the outliers that exist in real-world signals. Real data is used to compare the performance of both the Gaussian and the /spl alpha/-stable models. We demonstrate that the astable model gives a much better approximation to the noise signal than the Gaussian model. Furthermore, we study the problem of time delay estimation (TDE) and we demonstrate the shortcomings of TDE techniques based on second-order statistics when the noise is of S/spl alpha/S nature. We propose an alternative to second-order based methods, based on fractional lower-order statistics, and demonstrate the achieved improvement via simulation experiments.

Immersive audio systems are being envisioned for applications that include teleconferencing and telepresence; augmented and virtual reality for manufacturing and entertainment; air traffic control, pilot warning, and guidance systems; displays for the visually-impaired; distance learning; and professional sound and picture editing for television and film. The principal function of such systems is to synthesize, manipulate and render sound fields in real time. In this paper we examine several signal processing considerations in spatial sound rendering over loudspeakers. We propose two methods that can be used to implement the necessary filters for generating virtual sound sources based on synthetic head-related transfer functions with the same spectral characteristics as those of the real source.

A new representation of audio noise signals is proposed, based on symmetric /spl alpha/-stable (S/spl alpha/S) distributions in order to better model the outliers that exist in real signals. This representation addresses a shortcoming of the Gaussian model, namely, the fact that it is not well suited for describing signals with impulsive behavior. The /spl alpha/-stable and Gaussian methods are used to model measured noise signals. It is demonstrated that the /spl alpha/-stable distribution, which has heavier tails than the Gaussian distribution, gives a much better approximation to real-world audio signals. The significance of these results is shown by considering the time delay estimation (TDE) problem for source localization in teleimmersion applications. In order to achieve robust sound source localization, a novel time delay estimation approach is proposed. It is based on fractional lower order statistics (FLOS), which mitigate the effects of heavy-tailed noise. An improvement in TDE performance is demonstrated using FLOS that is up to a factor of four better than what can be achieved with second-order statistics.

Integrated media workstations are increasingly being used for creating, editing, and monitoring sound that is associated with video or computer-generated images. While the requirements for high quality reproduction in large-scale systems are well understood, these have not yet been adequately translated to the workstation environment. We discuss several factors that pertain to high quality sound reproduction at the desktop including acoustical considerations, signal processing requirements, and listener location issues. We also present a novel desktop system design with integrated listener-tracking capability that circumvents several of the problems faced by current digital audio and video workstations.

Immersive audio systems are being envisioned for applications that include teleconferencing and telepresence; augmented and virtual reality for manufacturing and entertainment; air traffic control, pilot warning, and guidance systems; displays for the visually- or aurally-impaired; home entertainment; distance learning; and professional sound and picture editing for television and film. The principal function of such systems is to synthesize, manipulate, and render sound fields in real time. In this paper we examine the limitations that are inherent in spatial sound delivery over loudspeakers and propose a method that generates virtual sound sources based on synthetic head-related transfer functions with the same spectral characteristics as those of the real source.

Numerous applications are currently envisioned for immersive audio systems. The principal function of such systems is to synthesize, manipulate, and render sound fields in real time. We examine several fundamental and technological limitations that impede the development of seamless immersive audio systems. Such limitations stem from signal processing requirements, acoustical considerations, human listening characteristics, and listener movement. We present a brief historical overview to outline the development of immersive audio technologies and discuss the performance and future research directions of immersive audio systems with respect to such limits. Last, we present a novel desktop audio system with integrated listener-tracking capability that circumvents several of the technological limitations faced by today's digital audio workstations.