This paper proposes a new method for pre-echo reduction in transform-based audio coding by controlling the temporal envelope of the waveform. The proposed method comprises two operating modes: temporal envelope flattening and temporal envelope correction of a target signal. The proposed method estimates signal levels with a low temporal resolution from side information using machine learning and converts them into a signal to be applied to the target signal to flatten and correct the temporal envelope. It also adjusts the signals to maintain signal continuity between the non-transient and transient frames. The proposed method differs from conventional methods in that it directly modifies the waveform before encoding and after decoding, which makes it useful as a new coding tool for legacy codecs. A subjective performance evaluation confirms that the proposed method uses fewer bits to provide sound quality equivalent to that of the short-window transform.1

For decades, linear predictive (LP) analysis of the real-valued time-domain signals has been developed for speech coding, analysis, and synthesis. Recently, the complex-valued frequency-domain LP coding was developed to enhance the estimation performance of the temporal envelope. To apply it for audio coding, a suitable representation for the complex-valued frequency-domain LP coefficients (CLPC) is required before quantization, but there was no efficient way to represent it. To address the problem, we propose efficient CLPC representations that retain some useful properties of conventional LPC representations. Through quantitative and qualitative evaluations, we demonstrate that our proposed representations increase quantization efficiency and improve audio coding performance.

Complex-valued frequency-domain linear predictive coding (CLPC) has been developed for audio coding. Recently, representations for efficiently quantizing CLPC coefficients have been proposed, including the complex immittance spectral frequency (CISF). The CISF has limitations in that it requires signalling the sequential information to eliminate ambiguity and the highest-order coefficient (HOC) for reconstructing the CLPC coefficients. This study developed a modified CISF-based method that eliminates the need for additional information by utilizing intermediate complex polynomial properties. Furthermore, a perceptual-metric-based codebook search was proposed to improve quantization efficiency. The experimental results show robust quantization performance, while listening tests demonstrate superior audio quality compared to MPEG-D USAC long TCX at 12 kbps.

Over the past decade, audio coding technology has seen standardization and the development of many frameworks incorporated with linear predictive coding (LPC). As LPC reduces information in the frequency domain, LP-based frequency-domain noise-shaping (FDNS) was previously proposed. To code transient signals effectively, FDNS with temporal noise shaping (TNS) has emerged. However, these mainly operated in the modified discrete cosine transform domain, which essentially accompanies time domain aliasing. In this paper, a unified noise-shaping (UNS) framework including FDNS and complex LPC-based TNS (CTNS) in the DFT domain is proposed to overcome the aliasing issues. Additionally, a modified polar quantizer with phase contrast control is proposed, which saves phase bits depending on the frequency envelope information. The core coding feasibility at low bit rates is verified through various objective metrics and subjective listening evaluations.

A 3D sound source localization (SSL) technique for multiple spherical arrays is proposed. Spherical arrays have been popularly used for direction of arrival (DoA) estimation, which serves as important prior knowledge for source separation, dereverberation, and binaural rendering for VR applications. In this work, a parametric 3D SSL technique is proposed that can detect multiple sources using multiple spherical array recordings. From the enriched information by using multiple arrays, we show that the parametric estimation of 3D positions is possible without scanning candidate positions in 3D space. To mitigate the problem of the ambiguous association between DoAs estimated from different arrays, a total covariance matrix including both auto- and cross-covariance matrices between arrays is utilized such that DoAs from arrays are automatically paired to uniquely localize 3D source positions. The joint parameter estimation is based on the generalized joint Schur decomposition of multiple matrices combined with a geometric projection robustly guiding the convergence of the proposed iteration algorithm. Simulations conducted in anechoic and reverberant room conditions reveal that the proposed technique can accurately determine multiple source positions in 3D space without ambiguities.

We present a novel, robust sound source localization algorithm considering back-propagation signals. Sound propagation paths are estimated by generating direct and reflection acoustic rays based on ray tracing in a backward manner. We then compute the back-propagation signals by designing and using the impulse response of the backward sound propagation based on the acoustic ray paths. For identifying the 3D source position, we use a well-established Monte Carlo localization method. Candidates for a source position are determined by identifying convergence regions of acoustic ray paths. Those candidates are validated by measuring similarities between back-propagation signals, under the assumption that the back-propagation signals of different acoustic ray paths should be similar near the ground-truth sound source position. Thanks to considering similarities of back-propagation signals, our approach can localize a source position with an averaged error of 0.55 m in a room of 7 m by 7 m area with 3 m height in tested environments. We also place additional 67 dB and 77 dB white noise at the background, to test the robustness of our approach. Overall, we observe a 7 % to 100 % improvement in accuracy over the state-of-the-art method.

The estimation of direction of arrivals (DoAs) from spherical microphone array data is one of the key issues in extracting source information from all-around audio recordings. One such technique is the eigenbeam estimation of signal parameters via the rotational invariance technique (EB-ESPRIT), which separates the signal subspace related to the stationary sound field and then directly estimates DoAs of multiple sound sources. EB-ESPRIT has been evolved in many different ways by involving different types of recurrence relations of spherical harmonics, all of which are able to identify DoAs of a limited number of sources that are noticeably smaller than the number of finite-order spherical harmonic coefficients recorded. In this work, we report that it is possible to go beyond the known limits of detectable sources. The proposed formula is also based on conventional recurrence relations and probably permits to reach the ultimate limit by additional constraints of the signal parameters that can better exploit the highest-order coefficients. Monte-Carlo simulations conducted with various source positions and signal-to-noise ratios (SNRs) reveal that the proposed technique can detect more sources with insignificant loss in estimation performance and robustness.

Correlated or coherent sources can cause the localization performance of subspace-based beamformers to deteriorate. To solve this problem, various smoothing techniques have been proposed for the localization of multiple coherent sound sources. A common principle of smoothing techniques is to increase the rank of a covariance matrix by constructing multiple subarrays in the space, time, or frequency domain. The construction of such subarrays, however, requires the satisfaction of strong assumptions regarding the microphone positions or the temporal/spectral structures of the signals. In this paper, we propose a spherical harmonic smoothing technique that can perform smoothing in terms of spherical harmonic coefficients only. Unlike other smoothing techniques, the proposed technique constructs subarrays of spherical harmonic coefficients and uses them to increase the number of linearly independent observations in a signal subspace. Subarray construction in the spherical harmonics domain enables the accurate localization of multiple coherent sources even at a single frequency. The proposed technique can be applied to an arbitrary microphone array as long as the spherical harmonic coefficients can be measured up to a finite order.