Audio Signal Classification Using Duration for Automatic Presets

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Solution Overview

Problem

Electronic devices often require manual selection of audio presets for different types of audio content, leading to errors and suboptimal quality of experience due to user unawareness of available presets and incorrect selections.

Innovation Solution

An automated audio signal classifier using a trained machine learning model, specifically a neural network, that classifies audio signals based on metadata features such as duration, sample rate, and bit-depth to automatically apply optimal audio presets for loudspeakers, ensuring optimal quality of experience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual selection of audio presets is implemented, then users can control audio output quality, but user unawareness and incorrect selections lead to suboptimal quality of experience

Engineering Contradiction:

Improvequality of experienceVSAvoidmanual preset selection

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system automatically classifies audio content and applies appropriate presets without user intervention. The audio signal classifier autonomously analyzes audio signals, determines content type (music, voice, movie), and configures frequency control settings, eliminating the need for manual user selection and ensuring optimal quality of experience.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of preset selection is replaced with an automated machine learning-based classification system. The neural network model processes audio signals and automatically determines optimal presets, substituting human decision-making with an automated intelligent system that consistently delivers optimal audio quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If automated audio classification is implemented, then optimal audio presets can be applied automatically, but system complexity increases with machine learning model integration

Engineering Contradiction:

Improveaudio preset accuracyVSAvoidclassification system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The audio signal classifier serves multiple functions: it identifies audio content type, determines optimal presets, and controls frequency settings. By consolidating these functions into a single multi-functional system, the patent reduces overall device complexity while maintaining high preset accuracy through the universal application of machine learning classification.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentEP3563251B1Audio classifcation with machine learning model using audio duration

Publication Date: 2022.10.19 HEWLETT PACKARD DEVELOPMENT COMPANY LP

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AI summary

An audio signal classifier including a feature extractor to extract metadata from an audio signal, the metadata defining a plurality of features of the audio signal, the feature extractor to generate a feature vector including selected features of the audio signal, the selected features including a duration of the audio signal, and each selected feature having a feature value. A machine learning model trained to classify the audio signal as one of a plurality of audio signal classes based on the feature vector. The machine learning model to provide a plurality of class values based on the feature values, each class value corresponding to one of the plurality of audio signal classes, the plurality of class values together indicating the class of the audio signal.