While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises with other dominant yet less intrusive sounds. However, misalignment between the dominant sound and the noise—such as mismatched downbeats—often requires an excessive volume increase to achieve effective masking. Motivated by recent advances in cross-modal generation, in this work, we introduce an alternative method to acoustic masking, aiming to reduce the noticeability of environmental noises by blending them into personalized music generated based on user-provided text prompts. Following the paradigm of music generation using mel-spectrogram representations, we propose a Blending Noises into Personalized Music (BNMusic) framework with two key stages. The first stage synthesizes a complete piece of music in a mel-spectrogram representation that encapsulates the musical essence of the noise. In the second stage, we adaptively amplifying the generated music segment to further reduce noise perception and enhance the blending effectiveness, while preserving auditory quality. Our experiments with comprehensive evaluations on MusicBench, EPIC-SOUNDS, and ESC-50 demonstrate the effectiveness of our framework, highlighting the ability to blend environmental noise with rhythmically aligned, adaptively amplified, and enjoyable music segments, minimizing the noticeability of the noise, thereby improving overall acoustic experiences. Project page: https://d-fas.github.io/BNMusic_page/.

Sound zone algorithms control the inputs to a loudspeaker array such that spatially distinct zones, each with separate audio content, are created. This work proposes a sound zone approach which includes a model of human auditory perception in the optimization problem designing the loudspeaker control filters. The control filters are therefore optimized directly for human experience, rather than by proxy through sound pressure, as is done in typical approaches. The proposed optimization problem features a perceptually weighted constraint on the bright zone reproduction error, which allows the user of the algorithm to specify the desired bright zone quality. The proposed method achieves 2 to 4 dB of additional acoustic contrast and is expected to yield less distracting dark-zone interference for the same perceived quality when compared to a traditional approach.

We present a joint design of sound zone control filters and robust audio coding for wireless low frequency sound zones. The audio signal is filtered using sound zone control filters and encoded using a multiple-description coder. The control filters and the multiple-description coder are combined in a nested loop. The inner loop performs filtering for sound zone control and generates multiple descriptions using oversampling and closed-loop prediction. The outer loop performs noise shaping and guarantees a trade-off between robustness and quality of the descriptions. A closed-form expression for the optimal sound-zone control filters are provided, and a simulation study demonstrates that even at moderate packet loss rates, a significant gain is possible compared to not using multiple descriptions.

Low frequency personal sound zones can be created by controlling the sound pressure in separate spatially confined regions. The performance of a sound zone system using wireless communication may be degraded due to potential packet losses. In this paper, we propose robust FIR filters for low-frequency sound zone system by incorporating information about the expected packet losses into the design. A simulation study with eight loudspeakers surrounding two control regions shows that the proposed filters can improve the contrast and the sound quality when packet losses occur, with only a slight degradation in performance even when there is no packet loss. With the proposed filters, it is possible to gain 2 dB higher contrast on average across the frequency range 20-200Hz relative to the original filters when packet loss rate is 5%.

We present a method to maintain the subjective perception of volume of audio signals and, at the same time, reduce their absolute peak value. We focus on achieving this without compromising the perceived audio quality. This is specially useful, for example, to maximize the perceived reproduction level of loudspeakers where simply amplifying the signal amplitude, and hence their peak value, is limited due to already constrained physical designs. In particular, we minimize the absolute peak value subject to a constraint based on auditory masking. This limits the perceptual difference between the original and the modified signals. Moreover, this constraint can be tuned and allows to control the resulting audio quality. We show results comparing loudness and audio quality as a function of peak reduction. These results suggest that our method presents the best trade-off between loudness and audio quality when compared against classical methods based on compression and clipping.

Sound zones are generated to provide independent audio reproduction to multiple people in the same room using loudspeakers. In this article, sound zones are formulated in terms of a moving horizon framework. This framework allows the reproduction scenario to be time-varying and adapt to changes e.g. in the location of the zones or in the audio signal. The framework is tested using both simulated and measured room impulse responses from eight loudspeakers in a rectangular room. The performance is investigated using signals limited between 35-500 Hz, but the framework is not limited to a particular frequency range. The experimental results show that it is possible to gain on the order of 4 dB higher separation between the zones using the proposed framework, relative to a conventional time-invariant solution. This gain arises from knowledge about the audio content currently being reproduced in the zones, and it is obtained without deteriorating the reproduction accuracy or increasing the signal energy injected into the loudspeakers.

Sound zones are valuable in scenarios where multiple people are present in the same room but want to listen to individual audio content without wearing headphones. The purpose of sound zone methods is to minimize the acoustic leakage between the zones by controlling multiple loudspeakers. This requires knowledge of how the loudspeakers interact with the room and radiate sound to the zones. That interaction is characterized by the transfer functions between the loudspeakers and microphones sampling the sound field in the zones. In this paper, the effect on the acoustic separation due to inherent noise in in situ transfer function measurements is investigated. The attainable separation is analyzed in the frequency range 20-300 Hz by means of the eigenfunctions of a rectangular room. The concept of observable degrees of freedom is introduced to indicate the number of active eigenfunctions, which are different within the zones at a given frequency. Likewise, controllable degrees of freedom indicate whether each source can excite the active eigenfunctions independently. It is argued that high separation can be achieved when the observable degrees of freedom are fewer than the controllable, and the target sound field can be described by the observable degrees of freedom. However, to attain this high separation it is a requirement that the details in the transfer functions associated with these degrees of freedom can be resolved in the presence of the measurement noise. For both simulated and experimental conditions the transfer functions are estimated using Bayesian inference and the uncertainty in the estimates is used to automatically regularize the sound field control. This regularization is seen to improve the performance when the measurement noise is correlated between the microphones and have little effect when the noise is uncorrelated.

In this paper, we present a greedy subspace method for the acoustic echoes labeling problem, which occurs in applications such as source localization and room geometry estimation. The orthogonal projection into the null space of the microphones position matrix is used to filter and sort all possible combinations of echoes. A greedy strategy, based on the rank constraint of Euclidean distance matrices (EDMs), is used on the sorted subset of echo combinations to extract the feasible combinations. Numerical simulations using room impulse responses (RIRs) from shoe-box shaped rooms show that the method provides improvements in terms of computational complexity and the number of required measurements with respect to a recently published graph-based method.

Sound zones are two or more regions within a listening space where listeners are provided with personal audio. Acoustic contrast control (ACC) is a sound zoning method that maximizes the average squared sound pressure in one zone constrained to constant pressure in other zones. State-of-the-art time domain broadband acoustic contrast control (BACC) methods are designed for anechoic environments. These methods are not able to realize a flat frequency response in a limited frequency range within a reverberant environment. Sound field control in a limited frequency range is a requirement to accommodate the effective working range of the loudspeakers. In this paper, a new BACC method is proposed which results in an implementation realizing a flat frequency response in the target zone. This method is applied in a bandlimited low-frequency scenario where the loudspeaker layout surrounds two controlled zones. The performance is verified with experimental results in an acoustically damped room.