# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 1 Introduction  
Pansori music, originating in South Korea in the late seventeenth century, gained popularity among the privileged class by the mid-eighteenth century and has since been recognized as intangible heritage by UNESCO, highlighting the vital need for its preservation and transmission to future generations [1, 2]. This traditional art form involves two key performers--the singer and the drummer. The drummer maintains the rhythm and provides essential cues for the singer, who, in turn, narrates melodic stories. The performance is structured into two distinct parts: storytelling, where the singer describes various characters and conveys the emotional depth of the story, followed by a segment where the singer projects their voice from the diaphragm, resonating powerfully with the audience [3]. Pansori music is characterized by three primary sources: the drum, the drummer's voice, and the singer's voice. The drummer and drum sounds are repeated throughout the song, typically within short timeframes. The drum sounds vary, comprising three distinct types or a combination thereof. Beyond its cultural significance, the need for vocal note transcription in Pansori music is crucial. Capturing the intricate details of the vocal notes and rhythms is essential for both preserving the authenticity of thisart form and facilitating its transmission to future generations. Accurate transcription allows for a deeper understanding of Pansori music's complex structure and provides a basis for analysis and study, offering insights into the interplay between the drummer, the singer, and the vocal and rhythmic patterns. This transcription process becomes essential for preserving the essence and integrity of Pansori music while facilitating its study in current contexts. There are basically four levels of transcription; frame-level transcription, note-level transcription, stream-level transcription, and notation-level transcription. In this paper, we are concerned with frame-level transcription and note-level transcription. Frame-level transcription involves analyzing very short frames of audio to identify the pitch or fundamental frequency (F0), which has been already proposed for transcription of speech [4], singing voices [5], and musical instruments [6], while note-level transcription approaches estimate notes' onset and offset times including pitches [7; 8]. The note-level transcription can be estimated in two ways. The first way is to estimate the onset time, offset time, and pitch of the note directly from the model [9], and the second way is to estimate the fundamental frequency first and apply post-processing like median filtering [10] or the hidden Markov model (HMM) [11] in the second step. In this work, we used the latter approach to estimate the vocal notes of Pansori. First, we used a pixel-wise semantic segmentation framework to calculate the fundamental frequency (F0) and applied Viterbi decoding obtained from the first step to estimate the vocal notes of Pansori music. We call this semantic segmentation framework for F0 estimation the source task and Viterbi decoding-based post-processing for Pansori vocal note estimation the target task. The proposed attentional based encoder-decoder network model for F0 estimation presented in this research for the source task comprises a comparison of self-attention and channel attention with different loss functions like binary cross-entropy and focal loss [12]. Our selection for attention mechanisms is inspired and drawn from the foundational works of the field [13; 14; 15; 16]. The proposed model is designed to operate on a combined frequency and periodicity (CFP) representation [10], which offers a detailed and comprehensive frequency-periodicity view of the audio data, essential for accurate F0 estimation. This architecture encompasses a series of convolutional layers, with subsequent batch normalization and SELU activation functions, aimed at extracting hierarchical features from the input CFP representation.