At this point, I'm conflicted. The lyrics testing went well for me, but I'm not proficient in chords. Are the chord results really that poor? I wonder if I can use it to play my favorite songs in real life.
I tried a few songs and uploaded one of my favorite songs. The lyrics recognition result was excellent, but there were a few instances where some words were missing or incorrect. It would be great if there could be an option to edit the lyrics.
Based on our current tests, our algorithm shows significantly higher accuracy and robustness compared to traditional digital signal algorithms such as PEF, NCF, YIN, HPS, etc. Our team is working diligently, and we will release benchmark test data and results in the near future.
Thank you for providing error feedback. We will work hard to address it. Currently, the model-related data is relatively large, which may be related to network speed.
CREPE generally has high latency and error rates in instrument pitch recognition, especially for guitar instruments. Our team will release benchmark test data and results later.
A transformer-based network model, pitch tracking for musical instruments.
The timbre of musical notes is the result of various combinations and transformations of harmonic relationships, harmonic strengths and weaknesses, instrument resonant peaks, and structural resonant peaks over time.
It utilizes the transformer-based tuneNN network model for abstract timbre modeling, supporting tuning for 12+ instrument types.
It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations, can be provided to deep learning networks for training.
A library for audio and music analysis and feature extraction, which supports dozens of time-frequency analysis and transformation methods, as well as hundreds of corresponding time-domain and frequency-domain feature combinations, can be provided to the deep learning network for training, and can be used to study the classification, separation, music information retrieval (MIR), ASR and other tasks in the audio field.
A library for audio and music analysis and feature extraction, which supports dozens of time-frequency analysis and transformation methods, as well as hundreds of corresponding time-domain and frequency-domain feature combinations, can be provided to the deep learning network for training, and can be used to study the classification, separation, music information retrieval (MIR), ASR and other tasks in the audio field.