Neural Network Loop Recommendations in Digital Audio Workstations

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Solution Overview

Problem

Users of digital audio workstations (DAWs) face difficulty in finding musically suitable pre-recorded 'loops' among thousands of options for their compositions, as conventional systems lack effective recommendation methods.

Innovation Solution

A method utilizing a neural network to recommend compositional segments based on vector similarity, where compositional segments are represented as vectors in a vector space, allowing for the identification of similar segments by analyzing combinations used in other compositions, and updating recommendations as new segments are added.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If users manually browse through thousands of pre-recorded loops in the library, then they can find compositional segments for their composition, but it becomes difficult to find musically-suitable audio files due to the large number of options

Engineering Contradiction:

Improveease of finding compositional segmentsVSAvoidtime to find musically-suitable loops

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system automatically analyzes the user's composition and generates personalized loop recommendations without requiring manual browsing. The neural network model autonomously identifies musically-suitable compositional segments by analyzing the user's workspace content and generating relevant recommendations, allowing the system to serve itself rather than requiring user effort to navigate thousands of options

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors the user's composition and updates recommendations in real-time based on feedback from the user's musical choices. As users add or remove compositional segments, the neural network reanalyzes the workspace and adjusts the recommended loops accordingly, creating a dynamic feedback loop that improves recommendation quality over time

Inventive Principle:

Principle #23Feedback

2Productivity

If a neural network model is used to analyze composition content and generate recommendations, then relevant compositional segments can be identified efficiently, but the system complexity increases due to the need for training data and model infrastructure

Engineering Contradiction:

Improveefficiency of audio content generationVSAvoidsystem complexity for neural network implementation

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The neural network model serves multiple functions: it analyzes the user's composition content, generates recommendations, and continuously learns from user interactions. The same model infrastructure is used for both analysis and recommendation generation, reducing the need for separate complex systems while maximizing productivity through a single multi-functional AI component

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11593059B2Systems and methods for generating recommendations in a digital audio workstation

Publication Date: 2023.02.28 SOUNDTRAP AB

AI summary

A method includes displaying a user interface of a digital audio workstation, which includes a first region for generating a composition. The first region includes a first compositional segment that has been added to the composition by a user. Based on the first compositional segment, one or more recommended predefined compositional segments are identified and displayed in a second region. The method includes receiving the selection of a second compositional segment. The method includes adding the compositional segment to the composition.