-fect the computation of descriptors. This raises the ques-tion of the robustness of these descriptors across various audio encodings. We examine this assumption for the case of MFCCs and chroma features. In particular, we analyze their robustness to sampling rate, codec, bitrate, frame size and music genre. Using two

short, representative samples (or “audio thumbnails”) of selections of popular music. The system searches for structural redundancy within a given song with the aim of identifying something like a chorus or refrain. To isolate a useful class of features for performing such structure-based pattern

Chroma-based audio features, which closely correlate to the aspect of harmony, are a well-established tool in processing and analyzing music data. There are many ways of computing and enhancing chroma features, which results in a large number of chroma variants with different properties

chroma features even more robust to changes in timbre and instrumentation while keeping their discriminative power. Our idea is based on the generally accepted observation that the lower mel-frequency cepstral coefficients (MFCCs) are closely related to timbre. Now, instead of keeping the lower

Abstract—We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the Music

When considering the problem of audio-to-audio matching, determining musical similarity using low-level features such as Fourier transforms and MFCCs is an extremely difficult task, as there is little semantic information available. Full semantic transcription of audio is an unreliable

into a sequence of chroma-based audio features and then pattern matching techniques are applied to map the chroma features to chord labels. In this paper, we analyze the role of the feature extraction step within the recognition pipeline of various chord recognition procedures based on template matching

boundaries between contrasting musical parts. This paper gives an overview of state-of-theart methods for computational music structure analysis, where the general goal is to divide an audio recording into temporal segments corresponding to musical parts and to group these segments into musically meaningful

model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality

captures both melodic information and harmonic information and is robust to timbre variation. Tempo variation in the performance of a same song may cause dissimi-larity between them. In order to address this issue we extract beat sequences and combine them with chroma features to obtain beat synchronous