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The Timbre Toolbox: Extracting Audio Descriptors from Musical Signals
The Timbre Toolbox: Extracting audio descriptors from musical signals Geoffroy Peetersa) Institut de Recherche et Coordination Acoustique/Musique (STMS-IRCAM-CNRS), 1 place Igor-Stravinsky, F-75004 Paris, France Bruno L. Giordano Centre for Interdisciplinary Research on Music Media and Technology (CIRMMT), Schulich School of Music, McGill University, 555 Sherbrooke Street West, Montre´al, Quebe´c H3A 1E3, Canada Patrick Susini and Nicolas Misdariis Institut de Recherche et Coordination Acoustique/Musique (STMS-IRCAM-CNRS), 1 place Igor-Stravinsky, F-75004 Paris, France Stephen McAdams Centre for Interdisciplinary Research on Music Media and Technology (CIRMMT), Schulich School of Music, McGill University, 555 Sherbrooke Street West, Montre´al, Quebe´c H3A 1E3, Canada (Received 24 November 2010; revised 9 March 2011; accepted 12 March 2011) The analysis of musical signals to extract audio descriptors that can potentially characterize their timbre has been disparate and often too focused on a particular small set of sounds. The Timbre Toolbox provides a comprehensive set of descriptors that can be useful in perceptual research, as well as in music information retrieval and machine-learning approaches to content-based retrieval in large sound databases. Sound events are first analyzed in terms of various input representations (short-term Fourier transform, harmonic sinusoidal components, an auditory model based on the equivalent rectangular bandwidth concept, the energy envelope). A large number of audio descrip- tors are then derived from each of these representations to capture temporal, spectral, spectrotempo- ral, and energetic properties of the sound events. Some descriptors are global, providing a single value for the whole sound event, whereas others are time-varying. -
A Psychoacoustic Model with Partial Spectral Flatness Measure for Tonality Estimation
A PSYCHOACOUSTIC MODEL WITH PARTIAL SPECTRAL FLATNESS MEASURE FOR TONALITY ESTIMATION Armin Taghipour1, Maneesh Chandra Jaikumar2, and Bernd Edler1 1 International Audio Laboratories Erlangen, Am Wolfsmantel 33, 91058 Erlangen, Germany 2 Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany [email protected] ABSTRACT quantization Psychoacoustic studies show that the strength of masking is, quantization spectral among others, dependent on the tonality of the masker: the decompo¡ coding PCM sition coded effect of noise maskers is stronger than that of tone maskers. signal audio Recently, a Partial Spectral Flatness Measure (PSFM) was in- signal quantization troduced for tonality estimation in a psychoacoustic model for perceptual audio coding. The model consists of an Infi- nite Impulse Response (IIR) filterbank which considers the perceptual model spreading effect of individual local maskers in simultaneous masking. An optimized (with respect to audio quality and Fig. 1. Basic structure of a transform based audio encoder. computational efficiency) PSFM is now compared to a sim- A transform decomposes frames of the input audio signal in ilar psychoacoustic model with prediction based tonality es- their spectral components. The psychoacoustic model calcu- timation in medium (48 kbit/s) and low (32 kbit/s) bit rate lates estimated masking thresholds for the frames with which conditions (mono) via subjective quality tests. 15 expert lis- it controls quantization steps for the individual subbands. teners participated in the subjective tests. The results are de- picted and discussed. Additionally, we conducted the subjec- to the best possible extent. However, at medium and low bit tive tests with 15 non-expert consumers whose results are also rates, the estimated masking threshold has to be violated. -
Bigear: Inferring the Ambient and Emotional Correlates from Smartphone-Based Acoustic Big Data
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data Harishchandra Dubey∗, Matthias R. Mehly, Kunal Mankodiya∗ ∗Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA yDepartment of Psychology, University of Arizona, Tucson, AZ, USA [email protected], [email protected], [email protected] Abstract—This paper presents a novel BigEAR big data frame- in naturalistic settings. The psychologists had an access to the work that employs psychological audio processing chain (PAPC) audio data and also the estimated mood labels that can help in to process smartphone-based acoustic big data collected when the making clinical decisions about the psychological health of the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer participants. Recently, our team developed a novel framework from various activities such as laughing, singing, crying, arguing, named as BigEAR that employs a psychological audio pro- and sighing. These annotations are based on ground truth cessing chain (PAPC) to predict the mood of the speaker from relevant for psychologists who intend to monitor/infer the social DALs. The BigEAR application installed on the iPhone/iPad context of individuals coping with breast cancer. We pursued captures the audio data from ambient environment at random a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. instance throughout the day. The snippets are of small sizes In the state-of-the-art methods, psychologists and their team so that we do not record significant duration of a personal have to hear the audio recordings for making these inferences context. -
1.4 a Pure Data Spectro-Morphological Analysis Toolkit for Sound-Based Composition
eaw2015 a tecnologia ao serviço da criação musical 31 1.4 A Pure Data Spectro-Morphological Analysis Toolkit for Sound-Based Composition Gilberto Bernardes Sound and Music Computing Group, INESC TEC, Portugal Matthew E. P. Davies Sound and Music Computing Group, INESC TEC, Portugal Carlos Guedes Sound and Music Computing Group, INESC TEC, Portugal; NYU Abu Dhabi, Uniteted Arab Emirates descriptors in applications like Shazam [1] and Moodagent Abstract [2] take place during the implementation phase of the algorithm and are hidden from the system’s interface. This paper presents a computational toolkit for the real- However, creative applications like Echo Nest Remix API time and offline analysis of audio signals in Pure Data. [3], CataRT (Schwarz, 2006) and earGram (Bernardes et Specifically, the toolkit encompasses tools to identify al., 2013) give users access to audio descriptors and even sound objects from an audio stream and to describe encourage them to experiment with their organization in sound objects attributes adapted to music analysis and order to retrieve and generate different audio sequences composition. The novelty of our approach in comparison to and outputs. existing audio description schemes relies on the adoption of a reduced number of descriptors, selected based on However, even if many computationally-extracted audio perceptual criteria for sound description by Schaeffer, descriptors—in particular those computed from audio data Smalley and Thoresen. Furthermore, our toolkit addresses by simple means, commonly referred to as low-level audio the lack of accessible and universal labels in descriptors—measure musical or perceptual properties of computational sound description tasks by unpacking sound, they are not adapted to the terminology of musical applied terminology from statistical analysis and signal practice and are meaningless without understanding the processing techniques.