Popular GitHub Repositories for Frequencies

:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

:sound: :boy: :girl: :woman: :man: Speaker identification using voice MFCCs and GMM

? Using Deep Learning to Categorize Music as Time Progresses Through Spectrogram Analysis

From frequencies to feeling

Coursera Natural Language Procession Specialization

A diffusion model trained to generate musical pieces using the MIDI format. It introduces a pitch encoding based on frequencies, a multi-instruments-oriented model and a lightweight music-adapted version of the U-Net.

Getting Started X-NUCLEO-IKS01A1 on B-L072Z-LRWAN1 applies to regions under frequencies 923 MHz AS923

This article introduces artificial neural networks as a computational tool to utilize legacy archaeological data for precisely and accurately estimating dates of residential site occupation. The implementation of this deep learning algorithm can provide high-resolution demographic reconstructions of a study area from non-collection, non-invasive, and non-destructive data collection methods that only record frequencies of artifact types on the contemporary ground surface. The utility of this deep learning algorithm is presented through an example from the central Mesa Verde region in the northern US Southwest. Results show a properly trained artificial neural network predicts annual residential occupation with an average 93.5% accuracy from AD 450–1300. An annual demographic reconstruction of the central Mesa Verde region is also presented

NLP app for book content analysis: text samples, word frequencies, wordcloud, entity recognition (story characters) Here for front-end: https://bookglancer.streamlit.app/ #nlt #spacy

[Tokenization, Topic Modeling, Sentiment Analysis, Network of Bigrams] The purpose of this project is to see if text mining techniques can ease better analysis for categorizing movies with just the Descriptions while ignoring the Genre from the dataset, IMDB_movies.csv, which is stored under the data frame variable, movies_desc. Tokenization (TF-DF) was used to increase efficiency to analyze term frequencies in movie Descriptions so that the conceptual theme of a movie franchise would be determined even if a person has never watched any of the films. Create mixtures of terms that are correlated to every topic and the mixture of topics that distinguishes each document through Topic Modeling in the dataset, IMDB_movies.csv. Sentimental Analysis focused on Movies with Sentimental Clusters that were using bing and NRC lexicons to see how Sent

This Jupyter notebook is an interactive tool for processing natural language text. It segments text into sentences, performs word tokenization, counts word frequencies, timestamps each entry, and saves the results in JSON format. Ideal for NLP studies and text analysis

A real-time audio frequency visualization tool that transforms your microphone input into beautiful visual representations