The content-based retrieval of Western music has received increasing attention in recent years. While much of this research deals with monophonic music, polyphonic music is far more common and more interesting, encompassing a wide selection of classical to popular music. Polyphony is also far more complex, with multiple overlapping notes per time step, in comparison with monophonic music's one-dimensional sequence of notes. Many of the techniques developed for monophonic music retrieval either break down or are simply not applicable to polyphony.|
The first problem one encounters is that of vocabulary, or feature selection. How does one extract useful features from a polyphonic piece of music? The second problem is one of similarity. What is an effective method for determining the similarity or relevance of a music piece to a music query using the features that we have chosen? In this work we develop two approaches to solve these problems. The first approach, hidden Markov modeling, integrates feature extraction and probabilistic modeling into a single, formally sound framework. However, we feel these models tend to overfit the music pieces on which they were trained and, while useful, are limited in their effectiveness. Therefore, we develop a second approach, harmonic modeling, which decouples the feature extraction from the probabilistic sequence modeling. This allows us more control over the observable data and the aspects of it that are used for sequential probability estimation.
Our systems - the first of their kind - are able to not only retrieve real-world polyphonic music variations using polyphonic queries, but also bridge the audio-symbolic divide by using imperfectlytranscribed audio queries to retrieve error-free symbolic pieces of music at an extremely high precision rate. In support of this work we offer a comprehensive evaluation of our systems.