kodhandarama Goto Github PK

asw_perception

audio_analysis

Analysis of Carnatic music

cloudfirebasechat

code_snippets

codefundo_2019

https://www.codefundo.io/ https://codefundo.io/community.html

cognitive-radio-networks

domain_adapted-ser

ethereum_elections

fastspeech2

An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

git-practice-workshop

A practice repository created for GitHub workshop.

gst-tacotron

A PyTorch implementation of Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

imageai

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

members-list

A list of all those involved with PES Open Source!

mixed_emotions_llm

mlpack

mlpack: a scalable C++ machine learning library --

mythodex

pythonformachinelearning

PythonForMachineLearning

rail-fence-cipher

Cryptography project

real_estate_mlproject

Machine learning algorithms are used to determine the investibility of a particular estate.

select-tts-demo

ser-for-indian-english-using-wav2vec

spectrum-sensing

This project aims to use signal processing based features to train and validate machine-learning algorithms to improve spectrum sensing and related problems in cognitive radios. We used differential entropy, geometric power and LP- norm based features to train supervised ML algorithms and various deep neural networks. The noise process is assumed to follow a generalized Gaussian distribution, which is of practical relevance. Through experimental results based on real-world captured datasets, we show that the proposed method outperforms the energy-based approach in terms of probability of detection. The proposed technique is particularly useful under low signal-to-noise ratio conditions, and when the noise distribution has heavier tails.

temottsdemo

tensorflow-object-detection-api-tutorial-train-multiple-objects-windows-10

How to train a TensorFlow Object Detection Classifier for multiple object detection on Windows

test-wb

virtual-source-width-perception

We explore the auditory perceptual property of virtual source width (VSW) expansion of synthetic vowels /a/, /i/ and /u/ and compare it with that of natural vowels. We also modify the synthetic vowels by adding white noise at the excitation or at the output. We observe that continuous vowel spectra creates a more stable VSW perception than discrete spectra. We also observe that the listeners perceive predominantly one source in case of shaped noise vowel and two sources in the case of additive white noise vowel. This observation shows that the shape of additive noise spectra plays a significant role in perception. This is quantified through the perceptual degree of VSWe. Additionally, we see a consistent increase in the perceived source width when the degree of non-stationarity is increased.

zulip

Zulip server - powerful open source team chat

zulip-terminal

A terminal interface for Zulip.