On the feasibility of personal audio systems over a network of distributed loudspeakers

Abstract

Abstract-Personal audio reproduction systems deal with the creation of personal sound zones within a room without the necessity of using headphones. These systems use an array of loudspeakers and design the required filters at each loudspeaker in order to render the desired audio signal to each person in the room as free of interferences as possible. There are very interesting proposals in the literature that make use of circular or linear arrays, but in this paper we study the problem considering a network of distributed loudspeakers controlled by a set of acoustic nodes, which can exchange information through a network. We state the model of such a distributed system by considering the electro-acoustic paths between the loudspeakers and each microphone, and try to provide a minimum signalto-interference-and-noise ratio (SINR) to each zone, but constraining the emitted power of the loudspeakers to a maximum value (avoiding annoying feedback effects). We make use of optimization techniques to decide if, given a distribution of the loudspeakers and a location of the personal sound zones within the room, the system will be feasible. Simulations are done to support the use of the proposed optimization techniques.

Signal-to-Interference-Plus-Noise Ratio Based Optimization for Sound Zone Control

“…The control filters obtained from all methods were normalized and then scaled according to the power allocation method (23). The SINR constraints were selected to be relaxed enough such that all methods generated feasible control filters, so that the power allocation method could be used.…”

“…An analogous duality exists for sound field reproduction, but is seldom used. It is used in [23] for sound zone control, however the method places significant restrictions on the loudspeaker and microphone arrays.…”

Sound Zoning in an Ad-hoc Wireless Acoustic Sensor and Actuator Network

“…is an all zero matrix where the identity matrix is placed on the positions corresponding to indexes k and l. However, to compute these optimal PM filters Ŵ, all impulse responses h p kl have to be available at a central FC. This FC then has to solve the QCQP (5), which is an optimization problem with KM LS variables. This introduces a single point of failure, and furthermore, requires a high computational capacity in the FC.…”

“…1) Initialize W 0 randomly without violating any constraint in (5), set i ← 0 and q ← 1. 2) Solve the reduced dimensional QCQP to obtain w i+1 k,s for all the nodes:…”

“…Although ( 20) is much easier to solve than (5) since it has only LSM optimization variables, (23) still requires the knowledge of all impulse responses h p kl and current PM filters in the WASAN. Luckily (23) can also be computed from the error microphone signals as…”