Desarrollo y evaluación de métodos para reducir la reverberación en señales de voz.

La reverberación es el proceso físico que sucede cuando se producen reflexiones del frente de ondas, las cuales llegan al oyente de forma retardada, pudiendo degradar la calidad e inteligibilidad de una señal de voz. En los últimos años, la investigación sobre el procesamiento de señales de voz reverberante ha experimentado un progreso significativo, y estas técnicas están listas para ser incorporadas a nuestras aplicaciones cotidianas. En este TFG se planteará el estudio, desarrollo y evaluación de algunos algoritmos prácticos que puedan reducir el efecto perjudicial de la reverberación, partiendo de algoritmos clásicos e incorporando técnicas más modernas que incluyen el uso de redes neuronales.<br /

Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise

We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. We propose to model the target and residual signals after linear echo cancellation and dereverberation using a multichannel Gaussian modeling framework and to jointly represent their spectra by means of a neural network. We develop an iterative block-coordinate ascent algorithm to update all the filters. We evaluate our system on real recordings of acoustic echo, reverberation and noise acquired with a smart speaker in various situations. The proposed approach outperforms in terms of overall distortion a cascade of the individual approaches and a joint reduction approach which does not rely on a spectral model of the target and residual signals

Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise: Supporting Document

This technical report is the supporting document of our proposed approach basedon a neural network for joint multichannel reduction of echo, reverberation and noise [1]. First, werecall the model of the proposed approach. Secondly, we express the vectorized computation of echocancellation and dereverberation. Thirdly, we detail the complete derivation of the update rules.Fourthly we describe the computation of the ground truth targets for the neural network usedin our approach. Fifthly we detail the variant of the proposed approach where echo cancellationand dereverberation are performed in parallel. Sixthly we describe the variant of the proposedapproach where only echo cancellation is performed. Then we specify the recording and simulationparameters of the dataset, we detail the computation of the estimated early near-end componentsand we recall the baselines. Finally we give the results after each filtering step and providesestimated spectrogram examples by all the approaches