Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music
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.
Code (1)
Similar Papers 제목 키워드 기반
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representa…
Chunkingnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+1On the Use of Word Embeddings Alone to Represent Natural Language Sequences
To construct representations for natural language sequences, information from two main sources needs to be captured: (i) semantic meaning of individual words, and (ii) their compositionality. These two types of informati…
SentenceWord EmbeddingsPredefined Sparseness in Recurrent Sequence Models
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is typically induced starting from a dense mo…
Language ModelingLanguage ModellingWord EmbeddingsReturn of the RNN: Residual Recurrent Networks for Invertible Sentence Embeddings
This study presents a novel model for invertible sentence embeddings using a residual recurrent network trained on an unsupervised encoding task. Rather than the probabilistic outputs common to neural machine translation…
Machine TranslationSentenceSentence EmbeddingsTranslationEvery Smile is Unique: Landmark-Guided Diverse Smile Generation
Each smile is unique: one person surely smiles in different ways (e.g., closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics?…
Video Generation