Στη παρούσα διπλωματική εργασία αναπτύσσεται ένα πρόγραμμα σε Java που εναρμονίζει μία μονοφωνική ή πολυφωνική μελωδία, η οποία θα εισάγεται στο σύστημα με τη μορφή MIDI αρχείων. Η βασική τεχνική που χρησιμοποιείται είναι τα κρυμμένα μοντέλα Markov. Εισάγονται διάφορες βελτιώσεις που ενσωματώνουν γνώσεις θεωρίας μουσική στα κρυμμένα μοντέλα Μαρκόφ. Τα πειραματικά αποτελέσματα έδειξαν ότι μπορούν να βελτιώσουν την συνολική απόδοση. / In this diploma dissertation an automatic melody harmonization program is developed, written in Java. It can harmonize either a monophonic or a polyphonic melody contained in a MIDI file using the power of hidden Markov Models. We introduce several methods which incorporate musical knowledge into hidden markov models. Experiment results show higher performance in chord recognition than the initial approach.

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[pt] A extração de parâmetros musicais de gravações de áudio a partir do processamento do sinal musical viabiliza uma série de aplicações importantes no campo de análise e classificação de peças musicais. A Harmonia é um importante aspecto da estrutura musical, fazendo da seqüência de acordes de uma peça uma informação extremamente relevante em sua análise e parte fundamental do próprio registro gráfico da música. Este trabalho discute os elementos envolvidos na transcrição de harmonia musical assim como as ferramentas matemáticas e de processamento de sinais adequadas a um método automático de análise e identificação de acordes, propondo um modelo para um sistema capaz de transcrever seqüências de acordes a partir de gravações de áudio comerciais. O sistema proposto analisa um sinal de áudio e retorna as cifras correspondentes aos acordes que melhor representam o sinal / [en] The extraction of musical parameters from audio recordings enables a series of important applications in the field of musical analysis and classification. Harmony is a major issue on music structure, making the chord sequence of a musical piece an extremely relevant information on its analysis and a fundamental part of the musical graphic register. This work discusses the elements involved in musical harmony transcription as well as the mathematical and signal processing tools suitable to build an automatic method for the analysis and identification of chords, proposing a model capable of transcribing chord sequences from commercial audio recordings. The proposed system analyzes an audio signal and returns the symbols corresponding to the chords that best fit the signal.

This work deals with automatic chord recognition using neural networks. The problem was separated into two subproblems. The first subproblem aims to experimental finding of most suitable solution for a acoustic model and the second one aims to experimental finding of most suitable solution for a language model. The problem was solved by iterative method. First a suboptimal solution of the first subproblem was found and then the second one. A total of 19 acoustic and 12 language models were made. Ten training datasets was created for acoustic models and three for language models. In total, over 200 models were trained. The best results were achieved on acoustic models represented by convolutional networks together with language models represented by recurent networks with LSTM modules.