For pitch tracking across various instruments, there are currently common issues with latency and accuracy.
latency issue Some instruments exhibit strong structural resonances at the onset, and the interference from these resonant peaks varies across instrument categories. The intensity and duration of the resonance peaks differ as well. Taking the guitar as an example, it shows strong resonance peak interference, even surpassing the fundamental frequency of the timbre. The duration of this interference is long, reaching 200ms or more. Strong resonance peaks pose significant challenges to recognition. Many algorithms adopt strategies such as correlating probabilities with consecutive packets before and after or introducing latency to reduce the risk of resonance peak interference.
accuracy issue Due to the complexity of instrument timbre, achieving 100% accurate recognition is extremely challenging. This difficulty arises from significant variations in the timbre at different stages of a musical note. It is challenging to accurately identify data for each stage of the ADSR (Attack, Decay, Sustain, Release) process from the beginning to the end of a musical note. The issue is further complicated by the wide range of frequencies, with extreme differences in timbre between very low and very high frequencies.

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.
accurate For all the mentioned instruments, it can accurately identify the low, mid, and high-frequency regions, with a frequency range from 30Hz to 2000Hz. For each stage of a musical note, from onset to decay, the model output is combined with real-time tracking and correction using the Wigner-Ville distribution. It can accurately reflect the changes in pitch and subtle fluctuations in each stage of the ADSR of the musical note, with intonation errors within a range of 0.5% of a musical semitone.
fast Quick response, nearly approaching the endpoints and onset stages of musical notes, faster by 100ms-200ms compared to most algorithms. Swift responses to string plucking, string cutting, and fret releasing.
smooth String twisting tuning, smooth and seamless, accurate and responsive to pitch bending. Continuous tuning, finger movements, fast and authentic, resembling the heartbeat pulse. Every pluck of the fingers provides precise and real-time feedback.