PitchNet: A Fully Convolutional Neural Network for Pitch Estimation

08/14/2023
by   Jeremy Cochoy, et al.
0

In the domain of music and sound processing, pitch extraction plays a pivotal role. This research introduces "PitchNet", a convolutional neural network tailored for pitch extraction from the human singing voice, including acapella performances. Integrating autocorrelation with deep learning techniques, PitchNet aims to optimize the accuracy of pitch detection. Evaluation across datasets comprising synthetic sounds, opera recordings, and time-stretched vowels demonstrates its efficacy. This work paves the way for enhanced pitch extraction in both music and voice settings.

READ FULL TEXT
research
06/04/2018

Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook

Since the vocal component plays a crucial role in popular music, singing...
research
12/29/2019

A Comparative Study of Pitch Extraction Algorithms on a Large Variety of Singing Sounds

The problem of pitch tracking has been extensively studied in the speech...
research
09/03/2022

Identify The Beehive Sound Using Deep Learning

Flowers play an essential role in removing the duller from the environme...
research
02/24/2021

Deep Learning Approach for Singer Voice Classification of Vietnamese Popular Music

Singer voice classification is a meaningful task in the digital era. Wit...
research
01/20/2020

JVS-MuSiC: Japanese multispeaker singing-voice corpus

Thanks to developments in machine learning techniques, it has become pos...
research
04/08/2020

Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation

Singing voice detection is the task to identify the frames which contain...
research
10/22/2019

Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy

In recent years, deep learning has surpassed traditional approaches to t...

Please sign up or login with your details

Forgot password? Click here to reset