Sonic tech music shelf
New interactive music services have emerged, but many of them use proprietary file formats. Finally, we conduct a glass ceiling analysis to study the current limitations of the method, and possible directions for future work are proposed. Further evaluation of the algorithm is provided in the form of a qualitative error analysis and the study of the effect of key parameters and algorithmic components on system performance. A comparative evaluation of the proposed approach shows that it outperforms current state-of-the-art melody extraction systems in terms of overall accuracy. This leads to the development of new voicing detection, octave error minimization and melody selection techniques. We define a set of contour characteristics and show that by studying their distributions we can devise rules to distinguish between melodic and non-melodic contours. Our approach is based on the creation and characterization of pitch contours, time continuous sequences of pitch candidates grouped using auditory streaming cues. We present a novel system for the automatic extraction of the main melody from polyphonic music recordings. The toolkit also provides functions for displaying the cost matrix, the forward and backward paths, and any metadata associated with the recordings, which can be shown in real time as the alignment is computed.
#Sonic tech music shelf software#
The software is use- ful for content-based indexing of audio files and for the study of performance interpretation it can also be used in real-time for tracking live performances. In tests with Classical and Romantic piano music, the average alignment error was 41ms (median 20ms), with only 2 out of 683 test cases failing to align. Frames of audio are represented by a positive spectral difference vector, which emphasises note onsets in the alignment process. A forward path estimation algorithm constrains the alignment path so that dynamic time warp- ing can be performed with time and space costs that are linear in the size of the audio files. We present MATCH, a toolkit for aligning audio record- ings of different renditions of the same piece of music, based on an efficient implementation of a dynamic time warping algorithm. In our experimental results, about 86% retrieval accuracy was obtained. In addition, we carried out a "singing voice adaptation" using a speaker adaptation technique. To exploit linguistic constraints maximally, we described the recognition grammar using a finite state automaton (FSA) that accepts only lyrics in the database. The difference between lyrics recognition and general speech recognition is that the in- put lyrics are a part of the lyrics of songs in a database. Lyrics recognition from a singing voice is achieved by similar technology to that of speech recognition.
In order to develop a MIR system that uses lyrics and melody infor- mation, lyrics recognition is needed. Although the lyrics information is useful for retrieval, there have been few at- tempts to exploit lyrics in the user's input. All of these systems use only the melody information for retrieval. Recently, several music information retrieval (MIR) sys- tems have been developed which retrieve musical pieces by the user's singing voice. We show that the recognition of these diffcult chords in particular is substantially improved by the prior approximate transcription using NNLS.
The nature of some chords makes their identification particularly susceptible to confusion between fundamental frequency and partials. This is a significant increase over the top result (74%) in MIREX 2009. We achieve very good results of 80% accuracy using the song collection and metric of the 2009 MIREX Chord Detection tasks. The resulting NNLS chroma features are tested by using them as an input to an existing state-of-the-art high-level model for chord transcription. We do so by performing a prior approximate transcription using an existing technique to solve non-negative least squares problems (NNLS). In this paper we reverse this approach and seek to find chroma features that are more suitable for usage in a musically-motivated model.
Research on the front end of chord transcription algorithms has often concentrated on finding good chord templates to fit the chroma features. The choice of chord profiles and higher-level time-series modelling have received a lot of attention, resulting in methods with an overall performance of more than 70% in the MIREX Chord Detection task 2009. The automatic detection and transcription of musical chords from audio is an established music computing task.