https://dodiku.github.io/audio_noise_clustering/results/ ==> An experiment with a variety of clustering (and clustering-like) techniques to reduce noise on an audio speech recording.
Lecture: In this lecture we introduce the area of speech processing, discuss historical background and current trends. In the second half of the lecture we introduce the concept fo speech as a separate modality from text or images and foreshadow concepts from later lectures.
Lecture: In this lecture we discuss how to transform an audio signal into a form which is convenient for use in Speech Recognition and Synthesis. We discuss: how an audio wave is sampled and digitized; The Fourier Transform and the Discrete Fourier Transform and how they can be used to obtain the frequency spectrum of the signal; How to use the Short-Time-Fourier-Transform to represent sound as a Spectrogram; finally, we discuss the Mel-Scale and how to obtain a Mel-Spectrogram.
Seminar: In part 1 we will implement the Short-Time-Fourier-Transform and obtain a Mel-Spectrogram. In part 2 we will: recover a Spectrogram from a Mel-Spectrogram. Reconstruct the original audio signal via the Griffin-Lim algorithm and do some simple voice warping.
Homework: Audio-MNIST: Implement a Neural Network model to do simple digit classification based on a mel-spectrogram.
Vladimir Kirichenko - lectures, seminars, homeworks
Segey Dukanov - lecures, seminars, homeworks
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