Photonic Delay Systems as Machine Learning Implementations

Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potentially consume far less power than digital processors. So far they have been successfully employed for signal processing using the Reservoir Computing paradigm. In this paper we show that their range of applicability can be greatly extended if we use gradient descent with backpropagation through time on a model of the system to optimize the input encoding of such systems. We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach. The results presented here demonstrate that common gradient descent techniques from machine learning may well be applicable on physical neuro-inspired analog computers

Audio Effects Emulation with Neural Networks

[EN] This paper discusses if using Neural Networks we can develop model which emulates audio effects and also if it can stand up to traditional audio effect emulators. This report includes the comparison of the performance between Recurrent Neural Networks such as Long Short Term Memory and Gated Recurrent Unit, and also Convolutional Neural Networks. This paper also checks if the best performing network, dealing with a online stream of inputs, can produce its outputs without a significant delay, as the ones of traditional audio effect emulators. The networks were trained to emulate an EQ effect. The results compared the audio produced by the network with the audio we want the network to produce, which is the audio modified by the EQ. These results were compared quantitatively, calculating the absolute difference between the two audio and comparing the frequency spectrum; and qualitatively, checking if people could hear both audios as the same one. Long Short Term Memory turned out to be the ones which achieved the best results. However, they could not produce a stream of outputs without a significant delay nor an acceptable error.Del Tejo Catala, O. (2017). Audio Effects Emulation with Neural Networks. http://hdl.handle.net/10251/88860.TFG