In virtual reality (VR), sound sources are convolved with room impulse responses (RIRs) to create immersive and dynamic audio experiences. Assessing the quality of spatial audio synthesis in VR is challenging. Subjective listening tests are accurate, but they are time-consuming and costly. This paper introduces a novel objective quality metric to predict the listening quality (LQ) and localization accuracy (LA) of Ambisonic spatial audio at new positions using spatially interpolated or extrapolated first-order Ambisonic (FOA) RIRs based on known nearby FOA RIRs. The LQ and LA scores are computed based on the Kolmogorov-Smirnov test, to measure the similarity between segments of direct sound and reflections of reference and synthesized FOA RIRs. Results show that these scores strongly correlate with subjective test results, proving the reliability of the proposed method. A major advantage is that it predicts spatial audio quality directly from synthesized FOA RIRs, without requiring convolved signals or being affected by specific sound sources, providing a more practical and efficient solution for development.

This paper introduces a novel objective quality metric for predicting the impact on Listening Quality (LQ) and Localisation Accuracy (LA) of interpolated Directional Room Impulse Responses (DRIRs). The DRIRs in this paper are First Order Ambisonic (FOA) representations of the RIR. Interpolation is used to synthesise a DRIR at a new listening position using known DRIRs at neighbouring listening positions. The target application is spatial audio synthesis for Virtual Reality (VR) that adapts to the position of the listener. The LQ and LA scores are based on the Pearson’s Correlation Coefficient (PCC), comparing segments of reference and synthesised DRIRs. These scores compare well with results from other representative objective measures of Ambisonic audio quality using DRIRs convolved with sound sources. A benefit of the proposed approach is predicting the impact on spatial audio quality when using interpolated DRIRs.

In this article, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both “points” (single T-F points) and “regions” (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

This paper proposes the co-prime circular microphone array (CPCMA), which applies co-prime array theory to circular microphone arrays. Compared with a conventional uniform circular array (UCA) with the same number of microphones and radius, the CPCMA achieves beampatterns with a narrower main lobe and fewer side lobes, whilst also avoiding spatial aliasing, thus having no grating lobes above the Nyquist frequency of the UCA. Array gain (AG) results indicate that the CPCMA is better than the UCA at amplifying the desired target signal while suppressing noise from other directions for typical speech signal frequencies. Compared with a UCA with similar performance and the same spatial Nyquist frequency, the CPCMA significantly reduces the required number of microphones. Simulations also indicate advantages of the CPCMA in speech source DOA estimation under high noise and low reverberation, especially when there are simultaneous multiple sources.

In this paper, we compare the performance of two active dereverberation techniques using a planar array of microphones and loudspeakers. The two techniques are based on a solution to the Kirchhoff-Helmholtz Integral Equation (KHIE). We adapt a Wave Field Synthesis (WFS) based method to the application of real-time 3D dereverberation by using a low-latency pre-filter design. The use of First-Order Differential (FOD) models is also proposed as an alternative method to the use of monopoles with WFS and which does not assume knowledge of the room geometry or primary sources. The two methods are compared by observing the suppression of reflections off a single active wall over the volume of a room in the time and (temporal) frequency domain. The FOD method provides better suppression of reflections than the WFS based method but at the expense of using higher order models. The equivalent absorption coefficients are comparable to passive fibre panel absorbers.

Reproducing zones of personal sound is a challenging signal processing problem that has garnered considerable research interest in recent years. We introduce in this work an extended method to multizone soundfield reproduction that overcomes issues with speech privacy and quality. Measures of speech intelligibility contrast (SIC) and speech quality are used as cost functions in an optimization of speech privacy and quality. Novel spatial and (temporal) frequency domain speech masker filter designs are proposed to accompany the optimization process. Spatial masking filters are designed using multizone soundfield algorithms that are dependent on the target speech multizone reproduction. Combinations of estimates of acoustic contrast and long term average speech spectra are proposed to provide equal masking influence on speech privacy and quality. Spatial aliasing specific to multizone soundfield reproduction geometry is further considered in analytically derived low-pass filters. Simulated and real-world experiments are conducted to verify the performance of the proposed method using semi-circular and linear loudspeaker arrays. Simulated implementations of the proposed method show that significant SIC and speech quality is achievable between zones. A range of perceptual evaluation of speech quality mean opinion scores that indicate good quality are obtained while at the same time providing confidential privacy as indicated by SIC. The simulations also show that the method is robust to variations in the speech, virtual source location, array geometry, and number of loudspeakers. Real-world experiments confirm the practicality of the proposed methods by showing that good quality and confidential privacy are achievable.

In this paper, we investigate the effects of compensating for wave-domain filtering delay in an active speech control system. An active control system utilising wave-domain processed basis functions is evaluated for a linear array of dipole secondary sources. The target control soundfield is matched in a least squares sense using orthogonal wavefields to a predicted future target soundfield. Filtering is implemented using a block-based short-time signal processing approach which induces an inherent delay. We present an autoregressive method for predictively compensating for the filter delay. An approach to block-length choice that maximises the soundfield control is proposed for a trade-off between soundfield reproduction accuracy and prediction accuracy. Results show that block-length choice has a significant effect on the active suppression of speech.

When noise is directional instead of diffuse, the majority of conventional direction of arrival (DOA) estimation techniques suffer from performance degradation because of mismatched noise models. In this paper, a novel robust DOA estimation algorithm is developed as an initial investigation into DOA estimation of speech under directional non-speech interference (DNSI) and non-directional background noise (NDBN) using an acoustic vector sensor (AVS), a compact co-incident microphone array. Specifically, by defining an intersensor data ratio model in the bispectrum domain (BISDR), the relationship between the BISDR and the speech DOA cues are derived. By recursively estimating a priori local signal-to-interference ratio of the bispectrum (B-PriLSIR), a robust speech-dominated binary mask (SDBM) is estimated and thus the speech DOA cue is faithfully extracted. Experimental results with simulated and recorded data demonstrate that the proposed algorithm offers high DOA estimation accuracy for all angles and is robust against DNSI and NDBN.

A key goal of engineering education is to ensure students are adequately prepared to enter professional practice. Unfortunately, students have been found to bring to university significant misconceptions about the types of work engineers perform, which can have an adverse impact on their readiness to fully appreciate and benefit from course activities. This paper presents a case study of how iSee, a collaborative online platform that allows for video conferencing within a three-dimensional immersive virtual world, was used to host a careers fair event in which students learned about the engineering profession and about the various engineering majors available to them by networking and interacting with alumni and faculty. The planning and execution of the event are described, along with the pedagogical, technological, and logistical considerations and design decisions that were made. Preliminary results suggest the activity, despite being low cost, was effective at generating productive dialogue between participants that focused on what students could expect upon graduating in terms of employment prospects, as well as the academic pathways and other learning opportunities they needed to pursue to realize their goals.

This paper proposes two methods for providing speech privacy between spatial zones in anechoic and reverberant environments. The methods are based on masking the content leaked between regions. The masking is optimised to maximise the speech intelligibility contrast (SIC) between the zones. The first method uses a uniform masker signal that is combined with desired multizone loudspeaker signals and requires acoustic contrast between zones. The second method computes a space-time domain masker signal in parallel with the loudspeaker signals so that the combination of the two emphasises the spectral masking in the targeted quiet zone. Simulations show that it is possible to achieve a significant SIC in anechoic environments whilst maintaining speech quality in the bright zone.

This paper describes miniature and three-tiered B-format microphone array designs for accurate sound source localisation that are manufactured using 3D printing and MEMs devices. The implications of pressure gradient resolution reduction in spatial-temporal sampling on the accuracy of Direction of Arrival (DOA) estimation is analysed through simulated room impulse response measurements and characterized by the directional signal to noise ratio. It is shown how the capsule spacing can be optimally chosen based on microphone capsule sensitivity and the required DOA accuracy. Through this method a new three tiered B-format microphone array is proposed, where each tier optimally records 3D sound for a given frequency sub-band to achieve highly accurate DOA estimation for the full audible frequency range of 50 Hz to 20 kHz.

Accurate DOA estimation based on clustering the inter-sensor data ratios (ISDRs) of a single acoustic vector sensor (AVS), referred as AVS-ISDR, relies on reliable extraction of time-frequency points with high local signal-to-noise ratio (HLSNR-TFPs) and its performance degrades in noisy environments. This paper investigates deep neural networks (DNNs) trained with noisy-clean speech pairs under different SNR levels and noise types to improve the performance of AVS-ISDR in noise conditions. The DNNs is trained to learn characteristics reflecting the level of speech information at different TFPs, which helps to generate a reliable spectral mask for obtaining a noise-reduced spectral. Correspondingly, a robust DOA estimation algorithm named as AVS-DNN-ISDR has been developed. Experimental results verify the proposed DNN-based spectral mask improves the reliable HLSNR-TFPs extraction at different SNR levels. Results from simulations and real AVS recordings further validate AVS-DNN-ISDR achieving high DOA estimation accuracy even when the SNR is lower than 0dB.

Preserving audio scenes in the form of audio objects has become common in recent years. Object-based audio techniques provide more flexibility for personalized rendering as well as a more accurate audio object trajectory. For encoding and transmitting multiple audio objects in a lossy manner, a new compression framework for multiple simultaneously occurring audio objects is presented in this work. The proposed encoding approach is based on the intra-object sparsity (approximate k-sparsity). After establishing a quantitative measure of approximate k-sparsity, statistical analysis is employed to validate the proposed intra-object sparsity of audio objects. By exploring this intra-object sparsity, multiple simultaneously occurring audio objects are compressed into a mono downmix signal with side information. This downmix signal can be further compressed by legacy audio codecs. Meanwhile, the side information is transmitted in a lossless manner. The objective and subjective evaluations revealed that the proposed compression framework achieved better perceptual quality compared to an existing technique where up to eight audio objects are considered. The subjective evaluations also confirmed that the proposed approach is able to achieve scalable transmission according to the bandwidth while preserving the perceptual quality of both the individual audio objects and the spatial audio scenes.

This paper introduces a dereverberation algorithm based on Linear Prediction (LP) applied to the outputs of an Acoustic Vector Sensor (AVS). The approach applies adaptive beamforming to take advantage of the directional outputs of the AVS array to obtain a more accurate LP spectrum than can be obtained with a single channel or Uniform Linear Array (ULA) with a comparable number of channels. This is then used within a modified version of the Spatiotemporal Averaging Method for Enhancement of Reverberant Speech (SMERSH) algorithm derived for the AVS to enhance the LP residual signal. In a highly reverberant environment, the approach demonstrates a significant improvement compared to a ULA as measured by both the Signal to Reverberant Ratio (SRR) and Speech to Reverberation Modulation Energy Ratio (SRMR) for sources ranging from at 1m to 5m from the array.

Perceptually accurate representation of audio objects obtained from multi-track audio signals is desired for applications such as interactive soundfield rendering and browsing. Presented in this work is a scalable psychoacoustic analysis-by-synthesis approach to extract the perceptually dominant time-frequency audio objects from a multi-track audio signal. The proposed compression framework exploits sparsity in the perceptual time-frequency domain where up to eight audio objects can be efficiently encoded using only two audio mixtures with side information representing the origin of the time-frequency instances in the mixture signals. The proposed approach, judged by both objective and subjective tests, results in superior audio quality compared to existing techniques when encoding more than 5 audio objects.

By exploring the time-frequency (TF) sparsity property of the speech, the inter-sensor data ratios (ISDRs) of single acoustic vector sensor (AVS) have been derived and investigated. Under noiseless condition, ISDRs have favorable properties, such as being independent of frequency, DOA related with single valuedness, and no constraints on near or far field conditions. With these observations, we further investigated the behavior of ISDRs under noisy conditions and proposed a so-called ISDR-DOA estimation algorithm, where high local SNR data extraction and bivariate kernel density estimation techniques have been adopted to cluster the ISDRs representing the DOA information. Compared with the traditional DOA estimation methods with a small microphone array, the proposed algorithm has the merits of smaller size, no spatial aliasing and less computational cost. Simulation studies show that the proposed method with a single AVS can estimate up to seven sources simultaneously with high accuracy when the SNR is larger than 15dB. In addition, the DOA estimation results based on recorded data further validates the proposed algorithm.

This paper presents a template-based system for speaker independent key word spotting (KWS) in continuous speech that can help in automatic analysis, indexing, search and retrieval of user generated videos by content. Extensive experiments on clean speech confirm that the proposed approach is superior to a HMM approach when applied to noisy speech with different signal-to-noise ratio (SNR) levels. Experiments conducted to detect swear words, personal names and product names within a set of online user generated video blogs shows significantly better recall and precision results compared to a traditional ASR-based approach.

This letter presents a new Collaborative Blind Source Separation (CBSS) technique that uses a pair of location informed coincident microphone arrays to jointly separate simultaneous speech sources based on time-frequency source localization estimates from each microphone recording. While existing BSS approaches are based on localization estimates of sparse time-frequency components, the proposed approach can also recover non-sparse (overlapping) time-frequency components. The proposed method has been evaluated using up to three simultaneous speech sources under both anechoic and reverberant conditions. Results from objective and subjective measures of the perceptual quality of the separated speech show that the proposed approach significantly outperforms existing BSS approaches.

This paper presents a technique for low bit rate compression of three-dimensional (3D) audio produced by multiple loudspeaker channels. The approach is based on the time-frequency analysis of the localization of spatial sound sources within the 3D space as rendered by a multi-channel audio signal (in this case 16 channels). This analysis results in the derivation of a stereo downmix signal representing the original 16 channels. Alternatively, a mono-downmix signal with side information representing the location of sound sources within the 3D spatial scene can also be derived. The resulting downmix signals are then compressed with a traditional audio coder, resulting in a representation of the 3D soundfield at bit rates comparable with existing stereo audio coders while maintaining the perceptual quality produced from separate encoding of each channel.

This paper presents a psychoacoustic-based analysis-by-synthesis approach for compressing navigable speech sources. The approach targets multi-party teleconferencing applications, where selective reproduction of individual speech sources is desired. Based on exploiting sparsity of speech in the perceptual time-frequency domain, multiple speech signals are encoded into one mono mixture signal, which can be further compressed using a standard speech codec. Using side information indicating the active speech source for each time frequency instant enables flexible decoding and reproduction. Objective results highlight the importance of considering perception when exploiting the sparse nature of speech in the time-frequency domain. Results show that this sparsity, as measured by the preserved energy level of perceptually important time-frequency components extracted from mixtures of speech signals, is similar in both anechoic and reverberant environments. The proposed approach is applied to a series of simulated and real reverberant speech recordings, where the resulting speech mixtures are compressed using a standard speech codec operating at 32 kbps. The perceptual quality, as judged both by objective and subjective evaluations, outperforms a simple sparsity approach that does not consider perception as well as the approach that encodes each source separately. While the perceptual quality of individual speech sources is maintained, subjective tests also confirm the approach maintains the perceptual quality of the spatialized speech scene.

This paper pressents an analysis-by-synthesis coding architecture for compressing navigable speech sources. The proposed coding scheme encodes multiple overlapped speech sources recorded, for example, during a multi-participant meeting or teleconference, into a mono or stereo mixture signal that can be compressed with an existing speech coder. The individual speech sources can be separated from the received compressed mixture, which allows the listener to determine the active sources and their spatial locations at the reproduction site. The approach was applied to the compression of a series of speech soundfields created from multiple clean speech sentences and real meeting recordings, where each sound-field contained four participants with up to three simultaneous speech sources. At a total bit rate of 48 kbps, the perceptual quality of each decoded speech source, as judged by subjective listening tests, was found to be significantly better than either a non-a-by-s approach or separate encoding of each source at the same overall total bit rate. Subjective listening tests also confirm that the quality of the spatialised speech scene is maintained as well.

This paper presents a new technique for preparing word templates to improve the performance of dynamic time warping based keyword spotting. The proposed technique selects one reference template from a small set of examples and in contrast to existing model based approaches does not require extensive training. Precision and recall results from applying the technique to template selection for use in searching for keywords in a clean speech database and within a set of user generated video blogs are superior to existing approaches used to select a template. As opposed to automatic speech recognition approaches, the technique is promising for use in searching for keywords that are not adequately represented in training databases.

This paper investigates the performance of a new technique for speech enhancement which combines Linear Predictive (LP) spectrum-based perceptual filtering to the recordings obtained from an Acoustic Vector Sensor (AVS). The technique takes advantage of the directional polar responses of the AVS to obtain a significantly more accurate representation of the LP spectrum of a target speech signal in the presence of noise when compared to single channel, omni-directional recordings. Comparisons between the speech quality obtained from the proposed technique and existing beamforming-based speech enhancement techniques for the AVS are made through Perceptual Evaluation of Speech Quality (PESQ) tests and Mean Opinion Score (MOS) listening tests. Results show significant improvements in PESQ and MOS scores of 0.2 and 1.6, respectively, for the proposed enhancement technique. Being based on a miniature microphone array, the approach is particular suitable for hands free communication applications in mobile telephony.

This paper investigates how the directional characteristics of an Acoustic Vector Sensor (AVS) can be used to separate speech sources. The technique described in this work takes advantage of the frequency domain direction of arrival estimates to identify the location, relative to the AVS array, of each individual speaker in a group of speakers and separate them accordingly into individual speech signals. Results presented in this work show that the technique can be used for real-time separation of speech sources using a single 20ms frame of speech, furthermore the results presented show that there is an average improvement in the Signal to Interference Ratio (SIR) for the proposed algorithm over the unprocessed recording of 15.1 dB and an average improvement of 5.4 dB in terms of Signal to Distortion Ratio (SDR) over the unprocessed recordings. In addition to the SIR and SDR results, Perceptual Evaluation of Speech Quality (PESQ) and listening tests both show an improvement in perceptual quality of 1 Mean Opinion Score (MOS) over unprocessed recordings.

This paper presents a new coding architecture for the compression of navigable speech soundfield zones. The proposed coding scheme encodes multiple speech soundfields, each representing different spatial zones, into a mono or stereo sound-field mixture signal that can be compressed with an existing speech or audio coder. The resulting compressed signals can be decoded back to individual soundfield zones. Objective and subjective testing results show that the approach successfully compresses up to 3 speech soundfields (each consisting of 4 individual speakers) at a bit rate of 48 kbps whilst maintaining the perceptual quality of each decoded soundfield zone.

This paper investigates multichannel speech enhancement for colocated microphone recordings based on Independent Component Analysis (ICA). Comparisons are made between co-located microphone arrays that contain microphones with mixed polar responses with traditional uniform linear arrays formed from omni-directional microphones. It is shown that polar responses of the microphones are a key factor in the performance of ICA applied to co-located microphones. Results from PESQ testing show a significant improvement in speech quality of ICA separated sources as a result of using an AVS rather than other types of microphone arrays.

With new social media technologies arising daily, this paper reports on a pilot user survey that studies how tertiary educated users are engaging with social media. The results indicate sporadic use of social media by the tertiary educated users studied; they are generally aware of the key social media sites and facilities, but are not actively utilizing these services. The reasons for, and the implications of a lack of tertiary educated users in the egalitarian and participatory environments intrinsic to social media are discussed. Further, the paper suggests potential technological barriers that might be at the root of such a lack of engagement amongst tertiary educated users.

Spatially squeezed surround audio coding (S3AC) has been previously shown to provide efficient coding with perceptually accurate soundfield reconstruction when applied to ITU 5.1 multichannel audio. This paper investigates the application of S3AC to the coding of Ambisonic audio recordings. Traditional ambisonics achieve compression and backward compatibility through the use of the UHJ matrixing approach to obtain a stereo signal. In this paper the relationship to Ambisonic B-format signals is described and alternative approaches that derive a stereo or mono-downmix signal based on S3AC are presented and evaluated. The mono-downmix approach utilizes side information consisting of spatial cues that are quantized based on novel source localization listening experiments. Objective and subjective tests demonstrate significant improvements in the localization of sound sources resulting from decoding the compressed B-format signals to a 5.1 speaker playback.

Automatic generation of metadata is an important component of multimedia search-by-content systems as it both avoids the need for manual annotation as well as minimising subjective descriptions and human errors. This paper explores the automatic attachment of basic descriptions (or dasiaTagspsila) to human motion held in a motion-capture database on the basis of a dynamic time warping (DTW) approach. The captured motion is held in the Acclaim ASF/AMC format commonly used in game and movie motion capture work and the approach allows for the comparison and classification of motion from different subjects. The work analyses the bone rotations important to a small set of movements and results indicate that only a small set of examples is required to perform reliable motion classification.

Recent research in speech localization and dereverberation introduced processing of the multichannel linear prediction (LP) residual of speech recorded with multiple microphones. This paper investigates the novel use of intra- and inter-channel speech prediction by proposing the use of a multichannel LP model derived from multivariate autoregression (MVAR), where current LP approaches are based on univariate autoregression (AR). Experiments were conducted on simulated anechoic and reverberant synthetic speech vowels and real speech sentences; results show that, especially at low reverberation times, the MVAR model exhibits greater prediction gains from the residual signal, compared to residuals obtained from univariate AR models for individually or jointly modelled speech channels. In addition, the MVAR model more accurately models the speech signal when compared to univariate LP of a similar prediction order and when a smaller number of microphones are deployed.

Several recent standards address virtual containers for rich multimedia content: collections of media with metadata describing the relationships between them and providing an immersive user experience. While these standards - which include MPEG-21 and TVAnytime - provide numerous tools for interacting with rich media objects, they do not provide a framework for streaming or delivery of such content. This paper presents the bitstream binding language (BBL), a format-independent tool that describes how multimedia content and metadata may be bound into delivery formats. Using a BBL description, a generic processor can map rich content (an MPEG-21 digital item, for example) into a streaming or static delivery format. BBL provides a universal syntax for fragmentation and packetization of both XML and binary data, and allows new content and metadata formats to be delivered without requiring the addition of new software to the delivery infrastructure. Following its development by the authors, BBL was adopted by MPEG as Part 18 of the MPEG-21 Multimedia Framework.

This paper presents a novel solution to multichannel spatial audio coding: spatial squeezing surround audio coding (S3AC). The S3AC scheme analyses a multichannel audio signal and downmixes it into a stereo signal pair containing both the monophonic properties of audio sources and their localization information; this avoids the need for side information. The approach uses time-frequency analysis of a spatial audio scene and exploits virtual sources and amplitude panning techniques to 'squeeze' 360deg of a horizontal soundfield to a 60deg stereo signal pair. In comparison with other spatial audio coding techniques, S3AC significantly advances in-band encoding of the localization information in the original sound scene and achieves accurate recoverability of dynamic localized sources.

MQF is a new XML based format designed to facilitate communication between disparate systems for applications involving multimedia query by content. Currently no standardized protocol exists which are able to provide flexibility in formulation of a query, such as the combination of any multimedia format (image, video, sound) to serve as query terms, combined with very complicated query conditions that can utilize a hierarchy of meta-search engines. In this work, we propose MQF as a flexible solution to serve as a communication format between a client and server for use in content based multimedia searching.

The Bitstream Binding Language (BBL) is a new technology developed by the authors and being standardized by MPEG, which describes how multimedia content and metadata can be mapped onto streaming formats. This paper describes a particular application of BBL - format-independent multimedia streaming. This means that streaming servers no longer require additional software modules in order to support new content formats as they are introduced. Instead, the server requires only a BBL description of the mapping between the content format and the stream, and any content in the new format may then be delivered by the streaming server. This approach is validated using the H.264/AVC format as an example, and performance data are provided.

This paper addresses efficiency issues identified in the Bitstream Syntax Description Language used by the MPEG-21 generic multimedia adaptation framework. In particular, when used to adapt modern content formats such as H.264/AVC, the time required for processing increases exponentially relative to the duration of the bitstream. In response, the paper proposes several additional features for the Bitstream Syntax Description Language which reduce the complexity of adaptation using BSDL to a linear function of bitstream duration. These features are implemented and validated using bitstreams of real-world length.

Generally, phonetic classification for low rate speech coding is restricted to either a simple binary voiced/unvoiced classification of entire speech frames, or alternatively, a more complicated estimation of the voicing for a set of frequency bands. A good compromise between these two techniques is estimation of a single cut-off frequency that separates the spectrum into voiced (below) and unvoiced (above) regions. Many existing cut-off frequency estimation methods use a fixed periodic spectrum to model voiced harmonics. However, due to pitch jitter, voiced harmonics do not always appear at regular intervals in the spectrum. In this paper a voicing cut-off estimation approach that combines speech energy, speech auto-correlation between frames and residual harmonic matching is proposed. Objective evaluation indicates that the algorithm is accurate and reliable. Subjective results obtained by embedding the algorithm in a low rate harmonic speech coder indicate that the technique is suitable for supporting high quality low rate speech synthesis. The proposed algorithm also requires relatively low complexity and introduces only a single frame of algorithmic delay.

This paper investigates waveform interpolation (WI) applied low bit rate wideband speech coding. An analysis of the evolutionary behaviour of wideband characteristic waveforms (CW) shows that direct application of the classical WI algorithm may not be appropriate for wideband speech. We propose a modification whereby CW quantisation is performed using classical WI decomposition for the low frequency region and noise modelling for the high frequency region. Wideband WI coders incorporating this modification and operating at 4 kbit/s and 6 kbit/s are described. Subjective testing of these coders shows that WI is a promising approach to low bit rate wideband speech compression.

This paper investigates waveform interpolation (WI) applied low bit rate wideband speech coding. An analysis of the evolutionary behaviour of wideband characteristic waveforms (CWs) shows that direct application of the classical WI algorithm may not be appropriate for wideband speech. We propose a modification whereby CW quantisation is performed using classical WI decomposition for the low frequency region and noise modelling for the high frequency region. Wideband WI coders incorporating this modification and operating at 4 kbps and 6 kbps are described. Subjective testing of these coders shows that WI is a promising approach to low bit rate wideband speech compression.

Linear prediction (LP) is the cornerstone of most modern speech compression algorithms. Previously it has been shown that incorporating a weighting function based on the simultaneous masking property of the ear into the calculation of the LP coefficients (SMWLPC) allows the filter to better model the unmasked sections of the input spectrum. This paper conducts a detailed analysis of the implementation of SMWLPC in low rate speech codecs. The analysis allows the cause of inconsistencies in the technique to be identified and solutions formulated. Experimental results show that when combined with the proposed changes, the SMWLPC technique is suitable for implementation in any low rate LP based speech codec and the net result is an improvement in the perceptual quality of synthesised speech for all speakers.

In this paper, we present new results on Temporal Decomposition (TD) applied to the Line Spectral Frequencies (LSFs) derived for wideband speech. The paper shows that by incorporating a dynamic programming search algorithm into TD, near transparent quantisation of wideband LSFs can be obtained at approximately 1 kbps. We also show that TD performs significantly better than Split Vector Quantisation at low bit rates. We propose that TD is a promising approach to low rate wideband speech coding for applications such as unicast streaming.