CREPE-guided note-level melody estimation and tonal-representation analysis for monophonic humming
Year: 2026
Type: article
Abstract: Humming is a convenient input for query-by-humming and music-education systems, but note boundaries are often weak because amateur performances contain glides, vibrato, short unvoiced gaps and intonation error. We combine the full CREPE pitch estimator with 44 frame-level pitch, energy, spectral-flux and voicing-context features. Separate RandomForest classifiers estimate onset and offset probabilities. Model and decoder parameters are selected within a recording-grouped 5 \(\times\) 4 nested cross-validation protocol, so each outer test recording is excluded from every selection step. After auditing the annotations, 38 MTG-QBH/Molina recordings contained 2152 reference notes. The primary nested-CV estimate achieved micro-averaged COnPOff, COnP and COn F -measures of 0.696, 0.842 and 0.889, respectively; the macro COnPOff was 0.685 (95% recording-bootstrap interval, 0.643–0.727). On the same 38 waveforms and metrics, the fixed official M4Singer-trained ROSVOT checkpoint obtained 0.171, 0.230 and 0.265 without Molina tuning. Frame-level raw pitch accuracy was 0.882 and overall accuracy was 0.761. Segmentation events were not concentrated at phrase boundaries: only 2.4% occurred within 150 ms of an operational phrase boundary, compared with 14.5% expected from temporal coverage. In a native zero-shot HumTrans test, COnPOff was 0.008; a separate validation-aligned sensitivity analysis using a frozen –0.18 s shift yielded 0.146. Audio- and note-derived pitch-class profiles showed high descriptive agreement (mean cosine similarity 0.978), but this is not interpreted as key-prediction accuracy because independent key labels are unavailable. A compact boundary model can recover useful note events from CREPE contours in small-data monophonic humming, provided that parameter selection is nested and annotations are audited. The large internal-to-external performance drop shows that this evidence does not establish corpus-independent generalisation. The 38-recording annotation set remains the limiting unit of independent evidence. Remaining errors occur mainly within continuously voiced passages, and tonal descriptors should remain separate from hard pitch correction.
Authors: Jieran Liu, Chaoxin Wang, Hongyu Jiang
Institution: Zhengzhou University of Industrial Technology
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,890
OpenAlex ID: W7224095022