3/23/2023 0 Comments Online audio filter designer![]() Specifically, sound professionals and music students were asked to manually equalize 41 audio segments. A similar approach is undertaken in Reference, where k-Nearest Neighbour (KNN) is used to implement a timbre equalizer based on user preference in terms of brightness, darkness and smoothness. In References, the authors describe a system that maps the gain of each frequency band with the user’s preferred equalizer settings as training data. The forward approach is also employed using a delayed copy of the input signal as input and the difference between the output given from the loudspeaker and the network as error. Finally, the communication scenario may or may not consider time-varying environmental conditions (e.g., mobile receiving stations), while in the audio field time-invariance is often assumed, thus, room impulse responses are measured and treated statically.Ī first attempt at the use of deep learning for audio equalization is found in Reference, where the authors use a Time Delay Neural Network (TDNN) to solve the problem of equalization, using the input sequence, delayed by a time unit, as input and the signal recorded by the microphone as output: the error between the input signal and the output of the network is used for the back-propagation algorithm. In communication systems the main goal is reducing symbol error rate, thus, allowing a robust classification of the symbols constellation, while in the audio field the goal is to achieve near-perfect audio quality taking psychoacoustic factors into consideration. This can represent an issue, as the equalizing filters must provide satisfying results at several listening positions, while with telecommunication devices, each one can adapt its equalizing filter depending on the incoming signal. While in communication systems equalizers are implemented at the receiving end, in the audio case they can only be implemented at the sound source. In Reference Genetic Algorithms (GA) are exploited for Adaptive Channel Equalization, in order to reduce the Inter Symbol Interference (ISI) present in the trasmission channel.Īlthough inspiring, these algorithms cannot be employed in the audio equalization scenario, as the two tasks differ in several aspects. ![]() Another interesting PSO approach is reported in Reference, where the PSO particles are used to obtain optimal poles and zeros of an IIR filter. This is shown to provide better results than Least Mean Square (LMS) and Recursive Least Square (RLS) techniques. In Reference, the authors use Particle Swarm Optimization (PSO) to equalize the impulse response of an optical fiber communication. Indeed, several novel techniques have been proposed for the design of equalizing filters for digital communications, relying on nonlinear methods. ![]() Since digital communication systems are subject to the multipath problem, that is, the sum of multiple reflections in a linear channel with multiple sources and receivers, it is worth investigating the literature for equalization techniques applied to this application field.
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