Automatic generation of sequences has been a highly explored field in the last years. In particular, natural language processing and automatic music composition have gained importance due to the recent advances in machine learning and Neural Networks with intrinsic memory mechanisms such as Recurrent Neural Networks. This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. The proposed approach considers music theory concepts such as transposition, and uses data transformations (embeddings) to introduce semantic meaning and improve the quality of the generated melodies. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically, measuring the tonality of the musical compositions.
翻译:过去几年来,自动生成序列一直是一个探索性很强的领域,特别是自然语言处理和自动音乐构成由于机器学习和神经网络最近的进展而变得日益重要,这些机械学习和神经网络有经常性神经网络等内在内存机制。本文件评估了不同类型的记忆机制(模拟细胞),并分析了它们在音乐构成领域的表现。拟议方法考虑了转置等音乐理论概念,并利用数据转换(组合)来引入语义含义,提高生成的旋律的质量。提出了一套量化指标,以自动评估拟议结构的性能,衡量音乐构成的品格。