hadryan,github

ciphey

Automatically decode encryptions without a key, decode encodings, and crack hashes

circle-player

This is a very simple html5 circle audio player. Not a lot of features but super easy to implement. Simply include one js file, one css file, and one line of jQuery to initialize.

circletype

A jQuery plugin for setting type on a cricle.

circular-music-progressbar

Beautiful Circular Progress Bar with album art for android

city-roads

Visualization of all roads within any city

city-sentiment-detector-bert

Classifying tweets from cities into Positive and Negative sentiment and determining happiness of that city.

classical

Store and manage your classical music collection

classical-music-quiz

An Android app which lets the user play a classical music quiz game, where the audio feature has been implemented with the help of ExoPlayer library and Media Sessions

classification-in-pyspark-s-mllib-project

Genre classification Now it's time to leverage what we learned in the lectures to a REAL classification project! Have you ever wondered what makes us, humans, able to tell apart two songs of different genres? How we do we inherenly know the difference between a pop song and heavy metal? This type of classifcation may seem easy for us, but it's a very difficult challenge for a computer to do. So the question is, could an automatic genre classifcation model be possible? For this project we will be classifying songs based on a number of characteristics into a set of 23 electronic genres. This technology could be used by an application like Pandora to recommend songs to users or just create meaningful channels. Super fun! Dataset beatsdataset.csv Each row is an electronic music song. The dataset contains 100 song for each genre among 23 electronic music genres, they were the top (100) songs of their genres on November 2016. The 71 columns are audio features extracted of a two random minutes sample of the file audio. These features have been extracted using pyAudioAnalysis (https://github.com/tyiannak/pyAudioAnalysis). Your task Create an algorithm that classifies songs into the 23 genres provided. Test out several different models and select the highest performing one. Also play around with feature selection methods and finally try to make a recommendation to a user. For the feature selection aspect of this project, you may need to get a bit creative if you want to select features from a non-tree algorithm. I did not go over this aspect of PySpark intentionally in the previous lectures to give you chance to get used to researching the PySpark documentation page. Here is the link to the Feature Selectors section of the documentation that just might come in handy: https://spark.apache.org/docs/latest/ml-features.html#feature-selectors Good luck! Have fun :) My approach I decided to approach this analysis in 4 main steps. Create Baseline: Train and evaluate models on raw data without pre-treating it for outliers, skewness or negative values. This way we can clearly see what effect our transformations have on our analysis. Test treatments: Train and evaluate models on treated data (outliers, skewness and negative values) and compare to baseline (#1). Feature Selection: Select the best performing models from the previous two approaches and perform feature selection on it to fine tune it. Make a recommendation to a user: Create a scrip to make a recommendation to a user. I intentionally left this part of the project a bit ambiguous Source https://www.kaggle.com/caparrini/beatsdataset

classification-of-anxiety-in-adults

Abstract: With the rise in its prevalence and the human intervention falling short, mental health related issues will pose daunting challenges. Since anxiety-related disorders are some of the most common ones, researchers have developed various Machine Learning (ML) models to predict anxiety in humans, using various modalities such as audio-video, physiological signals and, etc. However, none of the previous studies have explored correlating clinically validated inventories to deduce anxiety with ML models and, most of these work has been conducted in the Global North. To contribute to the limited research, in this work, we propose an experimental study with Indian adults to explore the use of Electrodermal Activity (EDA) and Photoplethysmogram (PPG) signals to deduce subjective anxiety in correlation with standard State-Trait Anxiety Inventory (STAI). We also discuss our proposed evaluation pipelines for feature extraction and classification based on state-of-the-art research.

classification-of-audio-data-into-song-genres-

Here,I have used data derived from echonest. The project uses PCA for feature engineering followed by randomforest and logistic regression for classification.

classification-of-musical-genres

Classification of musical genres using svm classifier and LPC + MFCC.

classify-song-genres-from-audio-data

Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), you will train a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). You will first make use of pandas and seaborn packages in Python for subsetting the data, aggregating information, and creating plots when exploring the data for obvious trends or factors you should be aware of when doing machine learning. Next, you will use the scikit-learn package to predict whether you can correctly classify a song's genre based on features such as danceability, energy, acousticness, tempo, etc. You will go over implementations of common algorithms such as PCA, logistic regression, decision trees, and so forth. This project lets you apply what you learned in Supervised Learning with scikit-learn, plus data preprocessing, dimensionality reduction, and machine learning using the scikit-learn package.

classify-song-genres-from-audio-data-1

Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), you will train a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). You will first make use of pandas and seaborn packages in Python for subsetting the data, aggregating information, and creating plots when exploring the data for obvious trends or factors you should be aware of when doing machine learning. Next, you will use the scikit-learn package to predict whether you can correctly classify a song's genre based on features such as danceability, energy, acousticness, tempo, etc. You will go over implementations of common algorithms such as PCA, logistic regression, decision trees, and so forth.

classify-song-genres-from-audio-data-2

Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), you will train a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). You will first make use of pandas and seaborn packages in Python for subsetting the data, aggregating information, and creating plots when exploring the data for obvious trends or factors you should be aware of when doing machine learning. Next, you will use the scikit-learn package to predict whether you can correctly classify a song's genre based on features such as danceability, energy, acousticness, tempo, etc. You will go over implementations of common algorithms such as PCA, logistic regression, decision trees, and so forth. This project lets you apply what you learned in Supervised Learning with scikit-learn, plus data preprocessing, dimensionality reduction, and machine learning using the scikit-learn package

classify-song-genres-from-audio-data-3

Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), I have train a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). I have first make use of pandas and seaborn packages in Python for subsetting the data, aggregating information, and creating plots when exploring the data for obvious trends or factors you should be aware of when doing machine learning. Next, I have use the scikit-learn package to predict whether you can correctly classify a song's genre based on features such as danceability, energy, acousticness, tempo, etc.

classifying-emotions-with-audio

classifying-song-genres-from-audio-data

Over the past few years, streaming services with huge catalogs have become the primary means through which most people listen to their favorite music. But at the same time, the sheer amount of music on offer can mean users might be a bit overwhelmed when trying to look for newer music that suits their tastes. For this reason, streaming services have looked into means of categorizing music to allow for personalized recommendations. One method involves direct analysis of the raw audio information in a given song, scoring the raw data on a variety of metrics. Today, we'll be examining data compiled by a research group known as The Echo Nest. Our goal is to look through this dataset and classify songs as being either 'Hip-Hop' or 'Rock' - all without listening to a single one ourselves. In doing so, we will learn how to clean our data, do some exploratory data visualization, and use feature reduction towards the goal of feeding our data through some simple machine learning algorithms, such as decision trees and logistic regression.

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