ARE you ? CS 229 Final Project

CS 229 = = Final Project Report SPEECH & NOISE SEPARATION

In this course project I investigated machine learning approaches on separating speech signals from background noise. Keywords—MFCC, SVM, noise separation, source separation, spectrogram

A Computational Model for Multi - Instrument Music Transcription CS 229 Final Project Report , Autumn 2013

The aim of our project is to build a model for multi-instrument music transcription. Automatic music transcription is the process of converting an audio wave file into some form of music notes representations. We propose a two-step process for an automatic multiinstrument music transcription system including timbre classification and source separation using probabilistic latent component analysis.

CS 229 Final Report : Learning to Select a Good Grasp

Daniel A . Woods CS 229 Final Project

1 Current methods model RNA sequence and secondary structure as stochastic context-free grammars, and then use a generative learning model to find the most likely parse (and, therefore, the most likely structure). As we learned in class, discriminative models generally enjoy higher performance than generative learning models. This implies that performance may increase if discriminative learning...

CS 224N/229: Joint Final Project: Large-Vocabulary Continuous Speech Recognition with Linguistic Features for Deep Learning

Until this day, automated speech recognition (ASR) still remains one of the most challenging tasks in both machine learning and natural language processing. ASR research faces data with high variability, which requires highly expressive models be built. Recently, deep neural networks (DNN) have been successfully applied to various fields, including speech recognition. In this course project, We...