Feasibility of using neural networks to obtain simplified capacity curves for seismic assessment

The selection of a given method for the seismic vulnerability assessment of buildings is mostly dependent on the scale of the analysis. Results obtained in large-scale studies are usually less accurate than the ones obtained in small-scale studies. In this paper a study about the feasibility of using Artificial Neural Networks (ANNs) to carry out fast and accurate large-scale seismic vulnerability studies has been presented. In the proposed approach, an ANN was used to obtain a simplified capacity curve of a building typology, in order to use the N2 method to assess the structural seismic behaviour, as presented in the Annex B of the Eurocode 8. Aiming to study the accuracy of the proposed approach, two ANNs with equal architectures were trained with a different number of vectors, trying to evaluate the ANN capacity to achieve good results in domains of the problem which are not well represented by the training vectors. The case study presented in this work allowed the conclusion that the ANN precision is very dependent on the amount of data used to train the ANN and demonstrated that it is possible to use ANN to obtain simplified capacity curves for seismic assessment purposes with high precision.info:eu-repo/semantics/publishedVersio

Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription

In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated

Final Research Report on Auto-Tagging of Music

The deliverable D4.7 concerns the work achieved by IRCAM until M36 for the “auto-tagging of music”. The deliverable is a research report. The software libraries resulting from the research have been integrated into Fincons/HearDis! Music Library Manager or are used by TU Berlin. The final software libraries are described in D4.5. The research work on auto-tagging has concentrated on four aspects: 1) Further improving IRCAM’s machine-learning system ircamclass. This has been done by developing the new MASSS audio features, including audio augmentation and audio segmentation into ircamclass. The system has then been applied to train HearDis! “soft” features (Vocals-1, Vocals-2, Pop-Appeal, Intensity, Instrumentation, Timbre, Genre, Style). This is described in Part 3. 2) Developing two sets of “hard” features (i.e. related to musical or musicological concepts) as specified by HearDis! (for integration into Fincons/HearDis! Music Library Manager) and TU Berlin (as input for the prediction model of the GMBI attributes). Such features are either derived from previously estimated higher-level concepts (such as structure, key or succession of chords) or by developing new signal processing algorithm (such as HPSS) or main melody estimation. This is described in Part 4. 3) Developing audio features to characterize the audio quality of a music track. The goal is to describe the quality of the audio independently of its apparent encoding. This is then used to estimate audio degradation or music decade. This is to be used to ensure that playlists contain tracks with similar audio quality. This is described in Part 5. 4) Developing innovative algorithms to extract specific audio features to improve music mixes. So far, innovative techniques (based on various Blind Audio Source Separation algorithms and Convolutional Neural Network) have been developed for singing voice separation, singing voice segmentation, music structure boundaries estimation, and DJ cue-region estimation. This is described in Part 6.EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC D

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Automatic recognition of Persian musical modes in audio musical signals

This research proposes new approaches for computational identification of Persian musical modes. This involves constructing a database of audio musical files and developing computer algorithms to perform a musical analysis of the samples. Essential features, the spectral average, chroma, and pitch histograms, and the use of symbolic data, are discussed and compared. A tonic detection algorithm is developed to align the feature vectors and to make the mode recognition methods independent of changes in tonality. Subsequently, a geometric distance measure, such as the Manhattan distance, which is preferred, and cross correlation, or a machine learning method (the Gaussian Mixture Models), is used to gauge similarity between a signal and a set of templates that are constructed in the training phase, in which data-driven patterns are made for each dastgàh (Persian mode). The effects of the following parameters are considered and assessed: the amount of training data; the parts of the frequency range to be used for training; down sampling; tone resolution (12-TET, 24-TET, 48-TET and 53-TET); the effect of using overlapping or nonoverlapping frames; and silence and high-energy suppression in pre-processing. The santur (hammered string instrument), which is extensively used in the musical database samples, is described and its physical properties are characterised; the pitch and harmonic deviations characteristic of it are measured; and the inharmonicity factor of the instrument is calculated for the first time. The results are applicable to Persian music and to other closely related musical traditions of the Mediterranean and the Near East. This approach enables content-based analyses of, and content-based searches of, musical archives. Potential applications of this research include: music information retrieval, audio snippet (thumbnailing), music archiving and access to archival content, audio compression and coding, associating of images with audio content, music transcription, music synthesis, music editors, music instruction, automatic music accompaniment, and setting new standards and symbols for musical notation

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs