Note-level automatic singing transcription, involving the extraction of onset, offset, and pitch information from a singing voice, is a crucial process in the field of Music Information Retrieval (MIR), The recent advancements in deep learning models have led to significant progress in this field. However, annotating a training dataset requires professional music expertise, and the entire annotation process is time-consuming and labor-intensive. Therefore, this field suffers from a severe data scarcity problem. To address this issue, we developed a singing transcription model based on wav2vec 2.0, a pretrained speech representation model. The model can learn from unlabeled speech and weakly-labeled singing data and use this knowledge to benefit the transcription task. The experiments showed that our proposed method achieves a significant improvement over previous approaches on various benchmarks. Moreover, additional experiments demonstrate that our method achieves competitive performance even with a small proportion of training data.

Creating a piece of music is difficult for people who have never been trained to compose. We present an automatic music generation system to lower the threshold of creating music. The system takes the user's humming as input and creates full music based on the humming melody. The system consists of five modules: 1) humming transcription, 2) melody generation, 3) broken chord generation, 4) accompaniment generation, and 5) audio synthesis. The first module transcribes the user's humming audio to a score, and then the melody generation module composes a complete melody based on the user's humming melody. After that, the third module will generate a broken chord track to accompany the full melody, and the fourth module will create more accompanying tracks. Finally, the audio synthesis module mixes all the tracks to generate the music. Through the user experiment, our system can generate high-quality music with natural expression based on the user's humming input.

This paper presents a novel system, named LingGe ("伶歌" in Chinese), to generate songs for ancient Chinese poems automatically. LingGe takes the poem as the lyric, composes music conditioned on the lyric, and finally outputs a full song including the singing and the accompaniment. It consists of four modules: rhythm recognition, melody generation, accompaniment generation, and audio synthesis. Firstly, the rhythm recognition module analyzes the song structure and rhythm according to the poem. Secondly, the melody generation module assembles the rhythm into the template and then generates the melody. Thirdly, the accompaniment generation module predicts the accompaniment in harmony with the melody. Finally, the audio synthesis module generates singing and accompaniment audio and then mixes them to obtain songs. The results show that LingGe can generate high-quality and expressive songs for ancient Chinese poems, both in harmony and rhythm.

Pop music generation has always been an attractive topic for both musicians and scientists for a long time. However, automatically composing pop music with a satisfactory structure is still a challenging issue. In this paper, we propose to leverage harmony-aware learning for structure-enhanced pop music generation. On the one hand, one of the participants of harmony, chord, represents the harmonic set of multiple notes, which is integrated closely with the spatial structure of music, the texture. On the other hand, the other participant of harmony, chord progression, usually accompanies the development of the music, which promotes the temporal structure of music, the form. Moreover, when chords evolve into chord progression, the texture and form can be bridged by the harmony naturally, which contributes to the joint learning of the two structures. Furthermore, we propose the Harmony-Aware Hierarchical Music Transformer (HAT), which can exploit the structure adaptively from the music, and make the musical tokens interact hierarchically to enhance the structure in multi-level musical elements. Experimental results reveal that compared to the existing methods, HAT owns a much better understanding of the structure and it can also improve the quality of generated music, especially in the form and texture.

Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing.Previous investigations prove that introducing a further pre-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects.However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap.After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap.To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase.The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all.Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks.Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.