scholarly journals Generative Process Tracking for Audio Analysis

2006 ◽  
Author(s):  
R. Radhakrishnan ◽  
A. Divakaran
Keyword(s):  
Audio Analysis ◽  
Generative Process

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström
Keyword(s):  
Reinforcement Learning ◽  
Short Term Memory ◽  
De Novo ◽  
De Novo Drug Design ◽  
Generative Process ◽  
New Methods ◽  
Multiparameter Optimization ◽  
Long Short Term Memory ◽  
New Compounds

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>

scholarly journals Audio-to-score singing transcription based on a CRNN-HSMM hybrid model

2021 ◽  
Vol 10 ◽  
Author(s):  
Ryo Nishikimi ◽  
Eita Nakamura ◽  
Masataka Goto ◽  
Kazuyoshi Yoshii
Keyword(s):  
Hybrid Model ◽  
Viterbi Algorithm ◽  
Language Model ◽  
Expressive Power ◽  
Acoustic Model ◽  
Musical Language ◽  
Musical Score ◽  
Singing Voice ◽  
Generative Process ◽  
Music Signal

This paper describes an automatic singing transcription (AST) method that estimates a human-readable musical score of a sung melody from an input music signal. Because of the considerable pitch and temporal variation of a singing voice, a naive cascading approach that estimates an F0 contour and quantizes it with estimated tatum times cannot avoid many pitch and rhythm errors. To solve this problem, we formulate a unified generative model of a music signal that consists of a semi-Markov language model representing the generative process of latent musical notes conditioned on musical keys and an acoustic model based on a convolutional recurrent neural network (CRNN) representing the generative process of an observed music signal from the notes. The resulting CRNN-HSMM hybrid model enables us to estimate the most-likely musical notes from a music signal with the Viterbi algorithm, while leveraging both the grammatical knowledge about musical notes and the expressive power of the CRNN. The experimental results showed that the proposed method outperformed the conventional state-of-the-art method and the integration of the musical language model with the acoustic model has a positive effect on the AST performance.

scholarly journals Models and scales for quality control: toward the definition of specifications (GOA-LOG) for the generation and re-use of HBIM object libraries in a Common Data Environment

2021 ◽  
Author(s):  
Raffaella Brumana ◽  
Chiara Stanga ◽  
Fabrizio Banfi
Keyword(s):  
Model Generation ◽  
Model Concept ◽  
Generative Process ◽  
New Construction ◽  
Phases Of Development ◽  
Data Environment ◽  
Definition Of ◽  
Object Models ◽  
Geometric Content ◽  
New Buildings

AbstractThe paper focuses on new opportunities of knowledge sharing, and comparison, thanks to the circulation and re-use of heritage HBIM models by means of Object Libraries within a Common Data Environment (CDE) and remotely-accessible Geospatial Virtual Hubs (GVH). HBIM requires a transparent controlled quality process in the model generation and its management to avoid misuses of such models once available in the cloud, freeing themselves from object libraries oriented to new buildings. The model concept in the BIM construction process is intended to be progressively enriched with details defined by the Level of Geometry (LOG) while crossing the different phases of development (LOD), from the pre-design to the scheduled maintenance during the long life cycle of buildings and management (LLCM). In this context, the digitization process—from the data acquisition until the informative models (scan-to-HBIM method)—requires adapting the definition of LOGs to the different phases characterizing the heritage preservation and management, reversing the new construction logic based on simple-to-complex informative models. Accordingly, a deeper understanding of the geometry and state of the art (as-found) should take into account the complexity and uniqueness of the elements composing the architectural heritage since the starting phases of the analysis, adopting coherent object modeling that can be simplified for different purposes as in the construction site and management over time. For those reasons, the study intends (i) to apply the well-known concept of scale to the object model generation, defining different Grades of Accuracy (GOA) related to the scales (ii) to start fixing sustainable roles to guarantee a free choice by the operators in the generation of object models, and (iii) to validate the model generative process with a transparent communication of indicators to describe the richness in terms of precision and accuracy of the geometric content here declined for masonry walls and vaults, and (iv) to identifies requirements for reliable Object Libraries.

scholarly journals Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models

2018 ◽  
Vol 7 ◽  
Author(s):  
Yuta Ojima ◽  
Eita Nakamura ◽  
Katsutoshi Itoyama ◽  
Kazuyoshi Yoshii
Keyword(s):  
Latent Variables ◽  
Language Model ◽  
Language Models ◽  
Sequential Dependency ◽  
Acoustic Model ◽  
Hierarchical Bayesian ◽  
Generative Process ◽  
Music Transcription ◽  
Automatic Music Transcription ◽  
Music Audio

This paper describes automatic music transcription with chord estimation for music audio signals. We focus on the fact that concurrent structures of musical notes such as chords form the basis of harmony and are considered for music composition. Since chords and musical notes are deeply linked with each other, we propose joint pitch and chord estimation based on a Bayesian hierarchical model that consists of an acoustic model representing the generative process of a spectrogram and a language model representing the generative process of a piano roll. The acoustic model is formulated as a variant of non-negative matrix factorization that has binary variables indicating a piano roll. The language model is formulated as a hidden Markov model that has chord labels as the latent variables and emits a piano roll. The sequential dependency of a piano roll can be represented in the language model. Both models are integrated through a piano roll in a hierarchical Bayesian manner. All the latent variables and parameters are estimated using Gibbs sampling. The experimental results showed the great potential of the proposed method for unified music transcription and grammar induction.

Fractal dimension pattern-based multiresolution analysis for rough estimator of speaker-dependent audio emotion recognition

2017 ◽  
Vol 15 (05) ◽  
pp. 1750042 ◽  
Author(s):  
Miao Cheng ◽  
Ah Chung Tsoi
Keyword(s):  
Fractal Dimension ◽  
Emotion Recognition ◽  
Multiresolution Analysis ◽  
Real Life ◽  
Emotional States ◽  
Acoustic Feature ◽  
Comparative Performance ◽  
Audio Analysis ◽  
The Arts ◽  
The Given

As a general means of expression, audio analysis and recognition have attracted much attention for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand the emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human–machine interfaces. Though there have been several the-state-of-the-arts auditory methods devised to audio recognition, most of them focus on discriminative usage of acoustic features, while feedback efficiency of recognition demands is ignored. This makes possible application of AER, and rapid learning of emotion patterns is desired. In order to make predication of audio emotion possible, the speaker-dependent patterns of audio emotions are learned with multiresolution analysis, and fractal dimension (FD) features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn the intrinsic characteristics of auditory emotions, while the utterance features are learned from FDs of each sub-band. Experimental results show the proposed method is able to provide comparative performance for AER.

In defense of the polycross

1987 ◽  
Vol 17 (12) ◽  
pp. 1624-1627 ◽  
Author(s):  
D. P. Fowler
Keyword(s):  
Genetic Parameters ◽  
Pollen Grains ◽  
Generative Process ◽  
Mating Design ◽  
Number Of Males

In Pinaceae species, except for Tsuga, the small number of pollen grains that actually compete in the generative process limits the possible reproductive bias that can result from using the polycross mating design for estimating genetic parameters. For species in which the number of competing pollen grains is 3 or less and where the number of males represented in the pollen mix is 20 or more, reproductive bias is within acceptable limits even if one male were to be favored over all others. Reproductive bias in other species, or where less than 20 males are represented in the mix, can be effectively reduced by diluting the pollen mix with dead pollen.

scholarly journals The World Is Not a Theorem

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1467
Author(s):  
Stuart Kauffman ◽  
Andrea Roli
Keyword(s):  
Set Theory ◽  
Mathematical Theory ◽  
Generative Process ◽  
Behavioral Adaptations ◽  
The World

The evolution of the biosphere unfolds as a luxuriant generative process of new living forms and functions. Organisms adapt to their environment, exploit novel opportunities that are created in this continuous blooming dynamics. Affordances play a fundamental role in the evolution of the biosphere, for organisms can exploit them for new morphological and behavioral adaptations achieved by heritable variations and selection. This way, the opportunities offered by affordances are then actualized as ever novel adaptations. In this paper, we maintain that affordances elude a formalization that relies on set theory: we argue that it is not possible to apply set theory to affordances; therefore, we cannot devise a set-based mathematical theory to deduce the diachronic evolution of the biosphere.

scholarly journals BIM AUTOMATION: ADVANCED MODELING GENERATIVE PROCESS FOR COMPLEX STRUCTURES

2017 ◽  
Vol IV-2/W2 ◽  
pp. 9-16 ◽  
Author(s):  
F. Banfi ◽  
S. Fai ◽  
R. Brumana
Keyword(s):  
Laser Scanning ◽  
Parametric Models ◽  
New Paradigm ◽  
Generative Process ◽  
Information Models ◽  
Historic Structures ◽  
Building Information ◽  
3D Elements ◽  
Scientific Results ◽  
Complex Forms

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.

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