Real-Time Pitch Estimation in Music Using Frequency Convolution Neural Network (FrCN) on Edge

Accurate monophonic pitch estimation is central to music and speech systems, with practical applications often requiring small models that remain robust to noise and timbre variations. This paper proposes a Frequency Convolution Network (FrCN) for fundamental frequency and voicing estimation from Variable-Q Transform (VQT) features. The model applies dilated one-dimensional convolutions along the log-frequency axis, giving a large frequency receptive field with only 17.8k parameters. This design allows the model to compare harmonic evidence across frequency while keeping the parameter count low. The model is trained with spectral-domain noise augmentation and frequency scaling augmentation, which encourage noise invariance and pitch equivariance. Experiments on MDB-stem-synth, PTDB-TUG, and MIR-1K show that FrCN improves over a CNN baseline in clean and noisy conditions. On clean in-domain evaluation, FrCN reaches 98.8% RPA50 on MDB, 93.1% on PTDB, and 97.5% on MIR-1K. At 0 dB SNR, FrCN keeps RPA50 above 80% on all three in-domain validation sets. The model is also competitive with recent pitch estimation systems while using far fewer parameters. These results show that frequency axis dilation is effective for low-parameter, noise-robust pitch estimation.