论文标题
分贝：通过对齐和集成众包象征性表示，改善流行音乐的音频和弦估计
DECIBEL: Improving Audio Chord Estimation for Popular Music by Alignment and Integration of Crowd-Sourced Symbolic Representations
论文作者
论文摘要
自动和弦估计（ACE）是音乐信息检索（MIR）的一项基本任务，并且在音乐性能和MIR研究中都有应用。该任务包括分割音乐录制或得分并为每个细分市场分配和弦标签。尽管这已经是年度基准评估Mirex已有10多年了，但ACE尚未解决问题，因为性能停滞不前，现代系统已经开始调整自己以进行主观培训数据。我们提出了Descibel，这是一种新的ACE系统，可利用广泛可用的MIDI和TAB表示形式，以仅从音频中改善ACE。从音频文件以及一组对应于同一流行音乐歌曲的MIDI和TAB文件，Decibel首先估计了和弦序列。对于音频，使用最先进的音频ACE方法。 MIDI文件与音频对齐，然后是MIDI和弦估计步骤。选项卡文件将转换为未列出的和弦序列，然后对齐到音频。接下来，Decibel使用数据融合将所有估计的和弦序列整合到一个最终输出序列中。 Decibel平均将所有经过测试的最先进的ACE方法提高了3％以上。该结果表明，来自异质象征音乐表现形式的音乐知识的整合是解决诸如ACE之类的挑战性mir任务的合适策略。
Automatic Chord Estimation (ACE) is a fundamental task in Music Information Retrieval (MIR) and has applications in both music performance and MIR research. The task consists of segmenting a music recording or score and assigning a chord label to each segment. Although it has been a task in the annual benchmarking evaluation MIREX for over 10 years, ACE is not yet a solved problem, since performance has stagnated and modern systems have started to tune themselves to subjective training data. We propose DECIBEL, a new ACE system that exploits widely available MIDI and tab representations to improve ACE from audio only. From an audio file and a set of MIDI and tab files corresponding to the same popular music song, DECIBEL first estimates chord sequences. For audio, state-of-the-art audio ACE methods are used. MIDI files are aligned to the audio, followed by a MIDI chord estimation step. Tab files are transformed into untimed chord sequences and then aligned to the audio. Next, DECIBEL uses data fusion to integrate all estimated chord sequences into one final output sequence. DECIBEL improves all tested state-of-the-art ACE methods by over 3 percent on average. This result shows that the integration of musical knowledge from heterogeneous symbolic music representations is a suitable strategy for addressing challenging MIR tasks such as ACE.
