# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 2 Related Work  
Several research studies have explored frame-level transcription techniques in the context of music analysis. In one study [18], a method is introduced for estimating multiple concurrent piano notes, addressing overlapping overtones with smooth autoregressive models. This approach mitigates pitch estimation errors in the presence of background noise, providing a comprehensive model for piano note transcription, validated using real piano recordings from the MAPS dataset [18]. Another study [10] tackles the challenge of training data for estimating multiple pitches in music and proposed a novel approach called combined frequency and periodicity (CFP). This method combines different features to enhance accuracy in simultaneous pitch estimation, demonstrating effectiveness in western polyphonic music even under audio distortions like filtering and compression. While these two studies represent conventional approaches to frame-level transcription, recent attention from researchers has shifted toward neural network-based methods [19; 20]. In [19], a supervised neural network model for polyphonic transcription on the MAPS dataset is introduced, incorporating an acoustic model and a music language model. The acoustic model, based on neural networks, estimates pitch probabilities in audio frames, while the recurrent neural network-based language model captures pitch correlations over time. In [20], the analysis of neural network-based frame-level piano transcription compares four different input representations, highlighting the importance of selecting the right input representation and fine-tuning hyperparameters, particularly the learning rate and its schedule, to improve frame-level transcription accuracy. Vocal melody transcription represents a specific aspect of frame-level transcription. In [21], a high-resolution network (HRNet) is employed to separate vocals from polyphonic music, and an encoder-decoder network is used to estimate vocal F0 values. The experimental results indicate that this HRNet-based singing voice separation method effectively minimizes accompaniment interference, surpassing other state-of-the-art algorithms in most cases. Another study ([22]) focuses on single-instrument transcription, demonstrating the ability to estimate onset and offset times with arbitrary time resolution, outperforming accuracy in the MAESTRO dataset [23]. Additionally, Ref. [24] introduces Omnizart, a Python toolkit covering the entire deep learning-based automatic music transcription (AMT) life cycle. Featuring a compact command-line interface for user convenience, Omnizart provides models for a diverse range of instruments, including solo and ensemble instruments, percussion, and vocals. It also includes models for chord recognition and beat/downbeat tracking, addressing crucial tasks in music information retrieval (MIR) related to automatic music transcription. Similarly, Ref. [25] tackles the challenging task of automatic music transcription (AMT), highlighting the unique difficulties of transcribing multiple instruments simultaneously with fine-scale pitch and timing precision. By employing a general-purpose transformer model trained through sequence-to-sequence transfer learning, the research successfully demonstrates multi-task AMT across various datasets. The note-based transcription approaches directly estimate notes, including pitches and onsets. This note-based transcription is one level higher than frame-level transcription. The work in [26; 27] estimates the pitches and onsets in a single architecture. One example for this is [28], which jointly estimates the different attributes of notes like pitch, intensity, onset, and duration. It estimates the properties of notes concurrently using harmonic temporal structured clustering. Note-level transcription can also be achieved after the post-processing in frame-level transcription. First, the fundamental frequency is estimated concurrently and applied post-processing to estimate the musical notes in the second step.