A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments

Abstract

This paper introduces a new method for detecting onsets, offsets, and transitions of the notes in real-time solo singing performances. It identifies the onsets and offsets by finding the transitions from one note to another by considering trajectory changes in the fundamental frequencies. The accuracy of our approach is compared with eight well-known algorithms. It was tested with two datasets that contained 130 files of singing. The total duration of the datasets was more than seven hours and had more than 41,000 onset annotations. The analysis metrics used include the Average, the F-Measure Score, and ANOVA. The proposed algorithm was observed to determine onsets and offsets more accurately than the other algorithms. Additionally, unlike the other algorithms, the proposed algorithm can detect the transitions between notes.

Deteksi Onset Gamelan Bebasis DWPT dan BLSTM

“…Proses ini bertujuan untuk merestrukturisasi data dengan melokalisasi data dalam jangka waktu-waktu tertentu sehingga dapat dilakukan proses analisis time-frequency. Proses ini menggunakan metode Hanning window dengan ukuran jendela 2048 yang umum digunakan dalam proses deteksi onset [7]. Pertama-tama sinyal masukkan suara x[n] disegmentasikan ke dalam bingkai 𝑾 = 𝟐.…”