# Singing-Voice Timbre Evaluations Based on Transfer Learning
## 1 Introduction
Breaking down the timbre into smaller dimensions and finally aggregating the overall score makes the task of evaluation timbre significantly less difficult, and this is the method used in this research study. In recent years, the rapid development of neural networks has led to further research on more abstract musical dimensions. Despite the increasing number of tasks in timbre, such as instrument timbre synthesis and timbre space modeling, there are still few tasks related to timbre evaluation, and even fewer tasks related to singing timbre evaluation. On the one hand, the automatic evaluation of vocal timbre is inherently difficult. On the other hand, available databases are limited. The publicly available databases in music analysis fields often have a clear scope of applicability with respect to the task, such as the Million Song Database (MSD) [4], which contains a variety of features such as pitch, loudness, musical genre, musician genre, etc., that are suitable for music information retrieval tasks; MUSDB18 [5]--a database for the design of source separation algorithms and evaluation reference database; EMOPIA database [6]--mainly used for research on music mood analysis, emotion classification, etc. Most datasets have files from instrument recordings, among which pianos are the most common. Singing-voice-type datasets number a few and few are related to timbre analyses. There is one dataset, CCM Music database [7], that is suitable for multiple types of music information retrieval tasks, and it contains various types of feature annotations, among which the singing dry-voice evaluation database contains singing-voice timbre scores, which meets the requirements of this experimental task. However, this dataset has problems such as a small volume and single dimension with respect to timbre evaluation, which makes direct machine learning more difficult for the complex task of automatic timbre evaluation. Another dataset in the database, Chinese Traditional Instrument Sound database, has a larger volume of data and includes more diverse evaluation dimensions with respect to timbre. Thus, the experiment was based on both of them using transfer learning to study the automatic evaluation of singing-voice timbre. This research explores the association between instrument timbre and singing-voice timbre from the perspective of experimental analysis, and we hope to provide research inspiration for future timbre studies and related auditory-type tasks. The purpose of this research is to develop an evaluation system for singing-voice timbre, which is a new direction of research in timbre. Currently, no research work related to singing-voice timbre evaluation has been conducted, and there are many problems such as a small collective amount of data and single evaluation index. This research constructed a deep learning network for transfer learning with respect to timbre evaluation with cross-entropy used as the evaluation metric, and the average loss cross-entropy predicted by the trained network was 1.013, with a high overlap between the predicted score regions and true score regions. Since there is no previous work for comparison, we designed a contrast model to prove the experiment's conclusions. Our research differs from previous work in terms of model structure, loss function, and evaluation metrics. The remainder of the research is organized as follows. Section 2 discusses the related work. Section 3 describes the materials and methods used in this experiment. Section 4 explains the transfer learning model and describes the contrast model used to validate the experimental conclusions. Section 5 reports the results of the experiments. Section 6 provides a discussion of the results of our work.