Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

Music and symbolic dynamics: the science behind an art

Music signals comprise of atomic notes drawn from a musical scale. The creation of musical sequences often involves splicing the notes in a constrained way resulting in aesthetically appealing patterns. We develop an approach for music signal representation based on symbolic dynamics by translating the lexicographic rules over a musical scale to constraints on a Markov chain. This source representation is useful for machine based music synthesis, in a way, similar to a musician producing original music. In order to mathematically quantify user listening experience, we study the correlation between the max-entropic rate of a musical scale and the subjective aesthetic component. We present our analysis with examples from the south Indian classical music system.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

Music-Theoretic Estimation of Chords and Keys from Audio

This paper proposes a new method for local key and chord estimation from audio signals. This method relies primarily on principles from music theory, and does not require any training on a corpus of labelled audio files. A harmonic content of the musical piece is first extracted by computing a set of chroma vectors. A set of chord/key pairs is selected for every frame by correlation with fixed chord and key templates. An acyclic harmonic graph is constructed with these pairs as vertices, using a musical distance to weigh its edges. Finally, the sequences of chords and keys are obtained by finding the best path in the graph using dynamic programming. The proposed method allows a mutual chord and key estimation. It is evaluated on a corpus composed of Beatles songs for both the local key estimation and chord recognition tasks, as well as a larger corpus composed of songs taken from the Billboard dataset.

Warrington Collegiate: LRC pop-up training area to enhance staff training and development

Warrington Collegiate wanted to locate an area of the Learning Resource Centre (LRC) and equip it with a smart board and ten laptops primarily for staff development. Since the pop-up seminar area has been created it has been used for Vado, Moodle, Quizdom and e-book training plus much more. From an empty corner in the LRC the addition of this technology has created a welcoming comfortable learning space. It has encouraged staff across all the curriculum areas to come into the LRC and extended their role in a place for high quality staff development.

Marketin Esperientziala: Kasu praktikoa. Pop-up Storeak.

Aditu askoren arabera, milurteko berriaren atarian marketin filosofia berri bat hasi zen loratzen: Marketin Esperientziala. Filosofia berri horrek paradigma aldaketa bat suposatzen du, fokua bezeroek enpresekin bizi dituzten esperientzia gogoangarrietan jartzen duena. Gaur egun, produktu eta zerbitzuak geroz eta antzekoagoak dira, kalitate eta prezioan oinarritutako lehia ez da nahikoa eta bezeroa geroz eta zorrotzagoa da. Horrek guztiak desberdintzeko bide berriak bilatu beharra dakar eta, horregatik, produktu eta zerbitzuez gain esperientziak sortu eta eskaintzea aukera paregabea bilakatu da enpresentzat, hau da, faktore emozionala kontuan hartzea. Gradu Amaierako Lan honek filosofia berri horren nondik norakoak aztertzea bilatzen du eta baita erosketa puntuan nola aplikatzen den ikertzea ere; izan ere, erosketa puntuek bezeroa, produktu/zerbitzua eta langileak atmosfera bakar batean biltzen dituzte. Horretarako, erosketa puntuan parte-hartzen duten esperientzia-eragile nagusiak aztertuko dira: zentzumenak, estetika eta diseinua eta langileen portaera. Ondoren, adibide gisa kasu praktiko eta esanguratsu bat aztertuko da: Pop Up Storeak edo, beste era batera esanda, aldi baterako establezimenduak.

Use of pop-up satellite archival tag technology to study postrelease survival of and habitat use by estuarine and coastal fishes: an application to striped bass (Morone saxatilis)

Pop-up satellite archival tags (PSATs) have been used to study movements, habitat use, and postrelease survival of large pelagic vertebrates, but the size of these tags has historically precluded their use on smaller coastal species. To evaluate a new generation of smaller PSATs for the study of postrelease survival and habitat use of coastal species, we attached Microwave Telemetry, Inc., X-tags to ten striped bass (Morone saxatilis) 94–112 cm total length (TL) caught on J hooks and circle hooks during the winter recreational fishery in Virginia. Tags collected temperature and depth information every five minutes and detached from the fish after 30 days. Nine of the ten tags released on schedule and eight transmitted 30% to 96% (mean 78.6%) of the archived data. Three tags were physically recovered during or after the transmission period, allowing retrieval of all archived data. All eight striped bass whose tags transmitted data survived for 30 days after release, including two fish that were hooked deeply with J hooks. The eight fish spent more than 90% of their time at depths less than 10 m and in temperatures of 6–9°C, demonstrated no significant diel differences in depth or temperature utilization (P>0.05), and exhibited weak periodicities in vertical movements consistent with daily and tidal cycles.