Multichannel active noise control (MCANC) is widely recognized as an effective and efficient solution for acoustic noise and vibration cancellation, such as in high-dimensional ventilation ducts, open windows, and mechanical structures. The feedforward multichannel filtered-x least mean square (FFMCFxLMS) algorithm is commonly used to dynamically adjust the transfer function of the multichannel controllers for different noise environments. The computational load incurred by the FFMCFxLMS algorithm, however, increases exponentially with increasing channel count, thus requiring high-end field-programmable gate array (FPGA) processors. Nevertheless, such processors still need specific configurations to cope with soaring computing loads as the channel count increases. To achieve a high-efficiency implementation of the FFMCFxLMS algorithm with floating-point arithmetic, a novel architecture based on multiple-parallel-branch with folding (MPBF) technique is proposed. This architecture parallelizes the branches and reuses the multiplier and adder in each folded branch so that the tradeoff between throughput and the usage of the hardware resources is balanced. The proposed architecture is validated in an experimental setup that implements the FFMCFxLMS algorithm for the MCANC system with 24 reference sensors, 24 secondary sources, and 24 error sensors, at a sampling and throughput rates of 25 kHz and 260 Mb/s, respectively.

Augmented Reality (AR) audio applications require headphones to be acoustically transparent so that real sounds can pass through unaltered for natural fusion with virtual sounds. In this paper, we consider a multiple source scenario for hear through (HT) equalization (EQ) using closed-back circumaural headsets. AR headset prototype (described in our previous study) is used to capture real sounds from external microphones and compute the directional HT filters using adaptive filtering. This method is best suited for single source scenarios as one best filter corresponding to the estimated source direction is optimally used for HT filtering. In this paper, we propose parametric HT EQ for multiple-source scenarios in time-frequency domain by estimating a sub-band Direction of Arrival (DOA) using neural networks (NN) and selecting the corresponding HT filters from a pre-computed database. Objective analysis using spectral difference (SD) is used to evaluate the performance of different HT EQ filters with open ear scenario used as a reference. Using dummy head measurements with bandlimited pink noise and real source signals, it was found that the proposed integrated system significantly improves the performance over the conventional HT system in multiple source scenarios.

Binaural audio plays an indispensable role in virtual reality (VR) and augmented reality (AR). Binaural audio recreates the sensation of the three dimensional auditory experience using Head- Related Transfer Functions (HRTFs). HRTFs are as unique as our fingerprint. To achieve an immersive audio experience, HRTFs measured from every particular user is required. Nowadays, the conventional methods for HRTF measurements requires a wellcontrolled environment, hardly any movement of the user, and projecting to the user a high level of unpleasant sound in a rather long duration. Such difficulties have greatly limited the use of individually measurement HRTFs and hinder the authenticity of immersive audio. To solve these problems, we proposed a fast and convenient HRTF measurement system that is an order of magnitude faster and more importantly, it does not place any constraints on the user's movement. With the help of a head-tracker and advanced adaptive signal processing algorithms, this system is able to achieve satisfactory HRTF measurement accuracy. In this demonstration, we will present a fast real-time HRTF acquisition system and show how the individualized HRTFs improve the audio experience in VR/AR applications.

Multichannel active noise control (MCANC) systems are commonly used in acoustic noise or vibration control, such as large-dimension ventilation ducts, open windows and mechanical structures. However, its computational load far exceeds the capabilities of digital signal processors (DSPs) and microcontrollers. Even the field programmable gate array (FPGA) cannot straightforwardly cope with the exponential increase in the computation load of MCANC systems. A novel architecture, called the multiple parallel branch with folding, is proposed for the J × J × M (J reference microphones, J secondary sources and Merror microphones) MCANC implementation with the floating-point arithmetic. This architecture addresses the tradeoff between throughput and hardware resource consumption by using parallel execution and folding. The proposed architecture is validated in an experimental setup that carries out a 4 × 4 × 4 multichannel filtered-x least mean square (FxLMS) algorithm achieving the sampling rate and throughput of 24 KHz and 18.4 Mbps, respectively.

Head related transfer function (HRTF) is widely used in 3D audio reproduction, especially over headphones. Conventionally, HRTF database is acquired at discrete directions and the acquisition process is time-consuming. Recent works have been proposed to improve HRTF acquisition efficiency via continuous acquisition. However, these HRTF acquisition techniques still require subject to sit still (with limited head movement) in a rotating chair. In this paper, we further relax the head movement constraint during acquisition by using a head tracker. The proposed continuous HRTF acquisition technique relies on the activation based normalized least-mean-square (ANLMS) algorithm to extract HRTF on the fly. Experimental results validated the accuracy of the proposed technique, when compared with the standard static acquisition technique.

In spatial audio analysis-synthesis, one of the key issues is to decompose a signal into primary and ambient components based on their spatial features. Principal component analysis (PCA) has been widely employed in primary component extraction, and shifted PCA (SPCA) is employed to enhance the primary extraction for input signals involving the inter-channel time difference. However, SPCA generally requires the primary components to come from one direction and cannot produce good results in the case of multiple directions. To solve this problem, we propose multi-shift PCA (MSPCA) by extending SPCA to multiple shifts. Two structures of MSPCA with different weighting methods are discussed. From the results of our simulations and listening tests, the proposed consecutive MSPCA with proper weighting is found to be superior to the conventional PCA and SPCA based primary extraction methods.

Individualization of head-related transfer functions (HRTFs) can be realized using the person's anthropometry with a pretrained model. This model usually establishes a direct linear or non-linear mapping from anthropometry to HRTFs in the training database. Due to the complex relation between anthropometry and HRTFs, the accuracy of this model depends heavily on the correct selection of the anthropometric features. To alleviate this problem and improve the accuracy of HRTF individualization, an indirect HRTF individualization framework was proposed recently, where HRTFs are synthesized using a sparse representation trained from the anthropometric features. In this paper, we extend their study on this framework by investigating the effects of different preprocessing and postprocessing methods on HRTF individualization. Our experimental results showed that preprocessing and postprocessing methods are crucial for achieving accurate HRTF individualization.

Spatial audio reproduction addresses the growing commercial need to recreate an immersive listening experience of digital media content, such as movies and games. Primary-ambient extraction (PAE) is one of the key approaches to facilitate flexible and optimal rendering in spatial audio reproduction. Existing approaches, such as principal component analysis and time-frequency masking, often suffer from severe extraction error. This problem is more evident when the sound scene contains a relatively strong ambient component, which is frequently encountered in digital media. In this Letter, we propose a novel PAE approach by estimating the ambient phase with a sparsity constraint (APES). This approach exploits the equal magnitude of the uncorrelated ambient components in the two channels of a stereo signal and reformulates the PAE problem as an ambient phase estimation problem, which is then solved using the criterion that the primary component is sparse. Our experimental results demonstrate that the proposed approach significantly outperforms existing approaches, especially when the ambient component is relatively strong.

The diversity of today's playback systems requires a flexible, efficient, and immersive reproduction of sound scenes in digital media. Spatial audio reproduction based on primary-ambient extraction (PAE) fulfills this objective, where accurate extraction of primary and ambient components from sound mixtures in channel-based audio is crucial. Severe extraction error was found in existing PAE approaches when dealing with sound mixtures that contain a relatively strong ambient component, a commonly encountered case in the sound scenes of digital media. In this paper, we propose a novel ambient spectrum estimation (ASE) framework to improve the performance of PAE. The ASE framework exploits the equal magnitude of the uncorrelated ambient components in two channels of a stereo signal, and reformulates the PAE problem into the problem of estimating either ambient phase or magnitude. In particular, we take advantage of the sparse characteristic of the primary components to derive sparse solutions for ASE based PAE, together with an approximate solution that can significantly reduce the computational cost. Our objective and subjective experimental results demonstrate that the proposed ASE approaches significantly outperform existing approaches, especially when the ambient component is relatively strong.

One of the key issues in spatial audio analysis and reproduction is to decompose a signal into primary and ambient components based on their directional and diffuse spatial features, respectively. Existing approaches employed in primary-ambient extraction (PAE), such as principal component analysis (PCA), are mainly based on a basic stereo signal model. The performance of these PAE approaches has not been well studied for the input signals that do not satisfy all the assumptions of the stereo signal model. In practice, one such case commonly encountered is that the primary components of the stereo signal are partially correlated at zero lag, referred to as the primary-complex case. In this paper, we take PCA as a representative of existing PAE approaches and investigate the performance degradation of PAE with respect to the correlation of the primary components in the primary-complex case. A time-shifting technique is proposed in PAE to alleviate the performance degradation due to the low correlation of the primary components in such stereo signals. This technique involves time-shifting the input signal according to the estimated inter-channel time difference of the primary component prior to the signal decomposition using conventional PAE approaches. To avoid the switching artifacts caused by the varied time-shifting in successive time frames, overlapped output mapping is suggested. Based on the results from our experiments, PAE approaches with the proposed time-shifting technique are found to be superior to the conventional PAE approaches in terms of extraction accuracy and spatial accuracy.

Primary-ambient extraction (PAE) has been playing an important role in spatial audio analysis-synthesis. Based on the spatial features, PAE decomposes a signal into primary and ambient components, which are then rendered separately. PAE is performed in subband domain for complex input signals having multiple point-like sound sources. However, the performance of PAE approaches and their key influences for such signals have not been well-studied so far. In this paper, we conducted a study on frequency-domain PAE using principal component analysis (PCA) in the case of multiple sources. We found that the partitioning of the frequency bins is very critical in PAE. Simulation results reveal that the proposed top-down adaptive partitioning method achieves superior performance as compared to the conventional partitioning methods.

Audio signals for moving pictures and video games are often linear combinations of primary and ambient components. In spatial audio analysis-synthesis, these mixed signals are usually decomposed into primary and ambient components to facilitate flexible spatial rendering and enhancement. Existing approaches such as principal component analysis (PCA) and least squares (LS) are widely used to perform this decomposition from stereo signals. However, the performance of these approaches in primary-ambient extraction (PAE) has not been well studied and no comparative analysis among the existing approaches has been carried out so far. In this paper, we generalize the existing approaches into a linear estimation framework. Under this framework, we propose a series of performance measures to identify the components that contribute to the extraction error. Based on the generalized linear estimation framework and our proposed performance measures, a comparative study and experimental testing of the linear estimation based PAE approaches including existing PCA, LS, and three proposed variant LS approaches are presented.

In spatial audio analysis-synthesis, one of the key issues is to decompose a signal into cue and ambient components based on their spatial features. Principal component analysis (PCA) has been widely employed in cue extraction. However, the performance of PCA based cue extraction is highly dependent on the assumptions of the input signal model. One of these assumptions is the input signal contains highly correlated cue at zero lag. However, this assumption is often unmet. To overcome this problem, time shifted PCA is proposed in this paper, which involves time-shifting the input signal according to the estimated inter-channel time difference (ITD) of the input signal before cue extraction. From our simulation and listening tests results, the proposed method is found to be superior to the conventional PCA based cue extraction method.