Low-level Audio Features Research Articles

Automated Music Emotion Recognition: A Systematic Evaluation

Automated music emotion recognition (MER) is a challenging task in Music Information Retrieval with wide-ranging applications. Some recent studies pose MER as a continuous regression problem in the Arousal-Valence (AV) plane. These consist of variations on a common architecture having a universal model of emotional response, a common repertoire of low-level audio features, a bag-of-frames approach to audio analysis, and relatively small data sets. These approaches achieve some success at MER and suggest that further improvements are possible with current technology. Our contribution to the state of the art is to examine just how far one can go within this framework, and to investigate what the limitations of this framework are. We present the results of a systematic study conducted in an attempt to maximize the prediction performance of an automated MER system using the architecture described. We begin with a carefully constructed data set, emphasizing quality over quantity. We address affect induction rather than affect attribution. We consider a variety of algorithms at each stage of the training process, from preprocessing to feature selection and model selection, and we report the results of extensive testing. We found that: (1) none of the variations we considered leads to a substantial improvement in performance, which we present as evidence of a limit on what is achievable under this architecture, and (2) the size of the small data sets that are commonly used in the MER literature limits the possibility of improving the set of features used in MER due to the phenomenon of Subset Selection Bias. We conclude with some proposals for advancing the state of the art.

Simultaneous Estimation of Chords and Musical Context From Audio

Chord labels provide a concise description of musical harmony. In pop and jazz music, a sequence of chord labels is often the only written record of a song, and forms the basis of so-called lead sheets. We devise a fully automatic method to simultaneously estimate from an audio waveform the chord sequence including bass notes, the metric positions of chords, and the key. The core of the method is a six-layered dynamic Bayesian network, in which the four hidden source layers jointly model metric position, key, chord, and bass pitch class, while the two observed layers model low-level audio features corresponding to bass and treble tonal content. Using 109 different chords our method provides substantially more harmonic detail than previous approaches while maintaining a high level of accuracy. We show that with 71% correctly classified chords our method significantly exceeds the state of the art when tested against manually annotated ground truth transcriptions on the 176 audio tracks from the MIREX 2008 Chord Detection Task. We introduce a measure of segmentation quality and show that bass and meter modeling are especially beneficial for obtaining the correct level of granularity.

DISAMBIGUATING SOUND THROUGH CONTEXT

A central problem in automatic sound recognition is the mapping between low-level audio features and the meaningful content of an auditory scene. We propose a dynamic network model to perform this mapping. In acoustics, much research is devoted to low-level perceptual abilities such as audio feature extraction and grouping, which are translated into successful signal processing techniques. However, little work is done on modeling knowledge and context in sound recognition, although this information is necessary to identify a sound event rather than to separate its components from a scene. We first investigate the role of context in human sound identification in a simple experiment. Then we show that the use of knowledge in a dynamic network model can improve automatic sound identification by reducing the search space of the low-level audio features. Furthermore, context information dissolves ambiguities that arise from multiple interpretations of one sound event.

Audio keywords generation for sports video analysis

Sports video has attracted a global viewership. Research effort in this area has been focused on semantic event detection in sports video to facilitate accessing and browsing. Most of the event detection methods in sports video are based on visual features. However, being a significant component of sports video, audio may also play an important role in semantic event detection. In this paper, we have borrowed the concept of the “keyword” from the text mining domain to define a set of specific audio sounds. These specific audio sounds refer to a set of game-specific sounds with strong relationships to the actions of players, referees, commentators, and audience, which are the reference points for interesting sports events. Unlike low-level features, audio keywords can be considered as a mid-level representation, able to facilitate high-level analysis from the semantic concept point of view. Audio keywords are created from low-level audio features with learning by support vector machines. With the help of video shots, the created audio keywords can be used to detect semantic events in sports video by Hidden Markov Model (HMM) learning. Experiments on creating audio keywords and, subsequently, event detection based on audio keywords have been very encouraging. Based on the experimental results, we believe that the audio keyword is an effective representation that is able to achieve satisfying results for event detection in sports video. Application in three sports types demonstrates the practicality of the proposed method.

Fusion of audio and motion information on HMM-based highlight extraction for baseball games

This paper aims to extract baseball game highlights based on audio-motion integrated cues. In order to better describe different audio and motion characteristics in baseball game highlights, we propose a novel representation method based on likelihood models. The proposed likelihood models measure the "likeliness" of low-level audio features and motion features to a set of predefined audio types and motion categories, respectively. Our experiments show that using the proposed likelihood representation is more robust than using low-level audio/motion features to extract the highlight. With the proposed likelihood models, we then construct an integrated feature representation by symmetrically fusing the audio and motion likelihood models. Finally, we employ a hidden Markov model (HMM) to model and detect the transition of the integrated representation for highlight segments. A series of experiments have been conducted on a 12-h video database to demonstrate the effectiveness of our proposed method and show that the proposed framework achieves promising results over a variety of baseball game sequences.

Beat tracking of musical performances using low-level audio features

This paper presents and compares two methods of tracking the beat in musical performances, one based on a Bayesian decision framework and the other a gradient strategy. The techniques can be applied directly to a digitized performance (i.e., a soundfile) and do not require a musical score or a MIDI transcription. In both cases, the raw audio is first processed into a collection of "rhythm tracks" which represent the time evolution of various low-level features. The Bayesian approach chooses a set of parameters that represent the beat by modeling the rhythm tracks as a concatenation of random variables with a patterned structure of variances. The output of the estimator is a trio of parameters that represent the interval between beats, its change (derivative), and the position of the starting beat. Recursive (and potentially real time) approximations to the method are derived using particle filters, and their behavior is investigated via simulation on a variety of musical sources. The simpler method, which performs a gradient descent over a window of beats, tends to converge more slowly and to undulate about the desired answer. Several examples are presented that highlight both the strengths and weaknesses of the approaches.

Audio Feature Extraction and Analysis for Scene Segmentation and Classification

Understanding of the scene content of a video sequence is very important for content-based indexing and retrieval of multimedia databases. Research in this area in the past several years has focused on the use of speech recognition and image analysis techniques. As a complimentary effort to the prior work, we have focused on using the associated audio information (mainly the nonspeech portion) for video scene analysis. As an example, we consider the problem of discriminating five types of TV programs, namely commercials, basketball games, football games, news reports, and weather forecasts. A set of low-level audio features are proposed for characterizing semantic contents of short audio clips. The linear separability of different classes under the proposed feature space is examined using a clustering analysis. The effective features are identified by evaluating the intracluster and intercluster scattering matrices of the feature space. Using these features, a neural net classifier was successful in separating the above five types of TV programs. By evaluating the changes between the feature vectors of adjacent clips, we also can identify scene breaks in an audio sequence quite accurately. These results demonstrate the capability of the proposed audio features for characterizing the semantic content of an audio sequence.