Real-Time Lossy Audio Signal Reconstruction Using Novel Sliding Based Multi-instance Linear Regression/Random Forest and Enhanced CGPANN

Abstract

This paper proposes a novel NeuroEvolutionary algorithm called Enhanced Cartesian Genetic Programming evolved Artificial Neural Network (ECGPANN) as a predictor for the lost signal samples in real time. Unlike traditional Cartesian Genetic Programming evolved Artificial Neural Network (CGPANN), the proposed algorithm introduces bi-chromosomal architecture instead of single chromosome to perform parallel evolution of topology with weights and architecture. This modification makes it suitable for obtaining global optimum solutions to predict both periodic and aperiodic lost samples at run-time. Sliding Window based Multi-instance Linear Regression (SW-MLR) and Sliding Window based Multi-instance Random Forest (SW-MRF) prediction algorithms are also exploited for the reconstruction of multiple missing samples. SW-MLR and SW-MRF being trained on fixed input/output cannot be utilized for random signal loss due to dynamic nature of number of output estimations needed at run-time. ECGPANN has the flexibility to produce variable number of outputs in real-time. Experimental results demonstrates the efficacy of the ECGPANN for both single and multi-sample loss with fix periodic and aperiodic noise using sliding window technique. The SNR improvement achieved ranges from 20 to 37 dB for periodic noise and 31–44 dB for aperiodic noise with signals having 16.6–50% samples missing. ECGPANN when compared in terms of its performance with the traditional CGPANN produced 4–5% improvement in prediction accuracy on average. The proposed ECGAPNN model is able to achieve a mean absolute error (MAE) of 0.051 (speech), 0.015 (guitar) and 0.038 (flute) for 16.6% lost/corrupted signals. MAE of 0.066 (speech), 0.020 (guitar) and 0.049 (flute) for 50% lost/corrupted data has been reported. The networks are trained and tested on audio speech signal and evaluated on music signals for its generality, with ECGPANN performing consistently better irrespective of the change in type of signals and demonstrated its robustness with change in number of missing samples in contrast to SW-MLR and SW-MRF. The ability to predict randomly variable number of missing samples make it applicable in real time.