In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

Digital processing of speech signals

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Convention: a philosophical study

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

This paper presents a hybrid music recommendation method that solves problems of two prominent conventional methods: collaborative filtering and content-based recommendation. The former cannot recommend musical pieces that have no ratings because recommendations are based on actual user ratings. In addition, artist variety in recommended pieces tends to be poor. The latter, which recommends musical pieces that are similar to users’ favorites in terms of music content, has not been fully investigated. This induces unreliability in modeling of user preferences; the content similarity does not completely reflect the preferences. Our method integrates both rating and content data by using a Bayesian network called an aspect model. Unobservable user preferences are directly represented by introducing latent variables, which are statistically estimated. To verify our method, we conducted experiments by using actual audio signals of Japanese songs and the corresponding rating data collected from Amazon. The results showed that our method outperforms the two conventional methods in terms of recommendation accuracy and artist variety and can reasonably recommend pieces even if they have no ratings.

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Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences

This paper describes sound source localization (SSL) based on deep neural networks (DNNs) using discriminative training. A naive DNNs for SSL can be configured as follows. Input is the frequency-domain feature used in other SSL methods, and the structure of DNNs is a fully-connected network using real numbers. The training fails because its network structure loses two important properties, i.e., the orthogonality of sub-bands and the intensity- and time-information saved in complex numbers. We solved these two problems by 1) integrating directional information at each sub-band hierarchically, and 2) designing a directional activator that could treat the complex numbers at each sub-band. Our experiments indicated that our method outperformed the naive DNN-based SSL by 20 points in terms of the block-level accuracy.

Sound source localization based on deep neural networks with directional activate function exploiting phase information

This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user's favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added.

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An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model

We propose a training method for multiple sound source localization (SSL) based on deep neural networks (DNNs). Such networks function as posterior probability estimator of sound location in terms of position labels and achieve high localization correctness. Since the previous DNNs' configuration for SSL handles one-sound-source cases, it should be extended to multiple-sound-source cases to apply it to real environments. However, a naive design causes 1) an increase in the number of labels and training data patterns and 2) a lack of label consistency across different numbers of sound sources, such as one and two-or-more-sound cases. These two problems were solved using our proposed method, which involves an independent location model for the former and an block-wise consistent labeling with ordering for the latter. Our experiments indicated that the SSL based on DNNs trained by our proposed training method out-performed a conventional SSL method by a maximum of 18 points in terms of block-level correctness.

Discriminative multiple sound source localization based on deep neural networks using independent location model

We provide a new solution to the problem of feature variations caused by the overlapping of sounds in instrument identification in polyphonic music. When multiple instruments simultaneously play, partials (harmonic components) of their sounds overlap and interfere, which makes the acoustic features different from those of monophonic sounds. To cope with this, we weight features based on how much they are affected by overlapping. First, we quantitatively evaluate the influence of overlapping on each feature as the ratio of the within-class variance to the between-class variance in the distribution of training data obtained from polyphonic sounds. Then, we generate feature axes using a weighted mixture that minimizes the influence via linear discriminant analysis. In addition, we improve instrument identification using musical context. Experimental results showed that the recognition rates using both feature weighting and musical context were 84.1% for duo, 77.6% for trio, and 72.3% for quartet; those without using either were 53.4, 49.6, and 46.5%, respectively.

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Kazunori Komatani

Papers

Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences

Sound source localization based on deep neural networks with directional activate function exploiting phase information

An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model

Discriminative multiple sound source localization based on deep neural networks using independent location model

Instrument identification in polyphonic music: feature weighting to minimize influence of sound overlaps