PitchTracking

Pitch tracking is the process of measuring and estimating the pitch of a moving object or sound. In sports analytics, it commonly refers to tracking pitched balls in baseball to determine release point, velocity, trajectory, and spin, enabling evaluation of performance, pitch design, and strategy. In audio and music technology, pitch tracking refers to estimating the fundamental frequency of an audio signal over time, enabling tasks such as tuning, transcription, and pitch correction.

Sports-oriented pitch tracking employs radar, stereo cameras, or a combination of data sources. Radar-based systems use Doppler measurements to estimate speed and spin, while optical-tracking systems use multiple high-speed cameras to reconstruct 3D trajectories. Sensor-based approaches may embed accelerometers or gyroscopes in the ball or player wearables. The outputs support classification of pitch types, analysis of grip and release, broadcast graphics, and coaching insights.

Audio pitch tracking uses algorithms to identify the dominant fundamental frequency across time. Classical methods include time-domain approaches such as autocorrelation and the YIN algorithm, and frequency-domain methods such as cepstrum or harmonic product spectrum. Modern systems increasingly rely on machine learning models that handle polyphony, vibrato, and noisy recordings. Applications include instrument tuning, vocal pitch correction, automatic transcription, and real-time pitch visualization.

History and examples: baseball pitch tracking products include PITCHf/x, TrackMan, and MLB Statcast, each combining radar, camera data, or other sensors to estimate velocity, spin, and trajectory. In music and audio, pitch tracking traces back to early pitch-detection algorithms and has evolved into software for digital audio workstations, live pitch correction, and music information retrieval.