CLARAPRINT: A Chord and Melody-Based Fingerprint for Efficient Retrieval of Western Classical Music Covers

Основные понятия

An engineering approach for efficiently retrieving cover versions of classical music works using a compact fingerprint based on chord and melody progressions.

Аннотация

This paper proposes a fingerprinting method called "claraprint" for efficient retrieval of cover versions of Western classical music works. The key insights are:

1. The chord progression and melody sequence of classical music works are sufficiently discriminative to serve as the basis for a fingerprint.

2. Even if the chord and melody extraction algorithms do not perfectly match the audio signal, the inaccuracies do not prevent the similarity search from returning valid results.

The authors introduce a new dataset of 100 classical music works with 5 cover versions each, and evaluate several chord and melody extraction algorithms to generate the claraprint. The results show that:

• Chord-based claraprints perform better than melody-based ones for this task.
• Combining multiple recordings of the same work to generate a reference claraprint significantly improves the retrieval accuracy.
• The claraprint generation and retrieval process is efficient in terms of ingestion and query times.

The authors conclude by suggesting future work to extend the benchmark, incorporate additional features, and handle more challenging classical music genres beyond the current dataset.

Статистика

The first 30 seconds of audio are not sufficient to capture the core fingerprint information for classical music works.
Using the full 120 seconds of audio greatly improves the performance.
The chord-extraction based algorithms (Chordino, Crema) outperform the melody-extraction based ones (Melodia, Piptrack).
Combining multiple recordings of the same work to generate a reference claraprint further boosts the retrieval accuracy.

Цитаты

"Given a sufficient amount of recorded time, the chord progression and the melody sequence of classical music works are discriminative enough to be used as the root of a fingerprint."
"Even if the chord extraction and melody extraction do not match accurately the audio signal, the inaccuracies do not prevent the similarity search from returning valid results."

Ключевые выводы из

Claraprint: a chord and melody based fingerprint for western classical music cover detection

by Mick... в arxiv.org 04-16-2024

https://arxiv.org/pdf/2009.10128.pdf

Дополнительные вопросы

How could the claraprint be extended to handle more challenging classical music genres beyond the current dataset, such as 12-tone, atonal or percussive works

To extend the claraprint to handle more challenging classical music genres like 12-tone, atonal, or percussive works, several adjustments and additions can be made. For 12-tone works, the claraprint could incorporate serialism principles to capture the unique tone rows and their transformations. Atonal works could involve algorithms that focus on pitch-class set analysis to identify recurring patterns. Percussive-only works might require rhythm-based fingerprinting techniques, emphasizing beat patterns and timbral characteristics instead of traditional harmonic and melodic elements. By integrating these specialized algorithms and features into the claraprint generation process, the system can adapt to the complexities of diverse classical music genres.

What other high-level musical features, beyond chord and melody, could be incorporated into the claraprint to make it more robust and discriminative

In addition to chord and melody, incorporating other high-level musical features can enhance the robustness and discriminative power of the claraprint. Some potential features to consider include:

Main Key: Including information about the main key of a musical work can provide additional context for matching and retrieval.
Duration: Factoring in the duration of the recording can help differentiate between pieces with similar chord and melody progressions.
Instrumentation: Annotated or extracted instrumentation details can contribute to distinguishing between different interpretations of the same work.
Dynamic Markings: Incorporating dynamics data can capture the expressive nuances of performances, aiding in identifying covers with varying interpretations.
Texture: Analyzing the texture of the music, such as monophonic, homophonic, or polyphonic, can offer further insights into the style and structure of the composition.
By integrating these features into the claraprint generation process, the system can create more comprehensive and detailed fingerprints for classical music cover detection.

How could the claraprint generation and retrieval process be further optimized to handle extremely large-scale classical music catalogs in real-world applications

To optimize the claraprint generation and retrieval process for handling extremely large-scale classical music catalogs in real-world applications, several strategies can be implemented:

Parallel Processing: Utilizing parallel computing techniques to distribute the workload and speed up the fingerprint generation process.
Indexing: Implementing efficient indexing methods to store and retrieve claraprints quickly, enabling fast search queries across a vast music database.
Incremental Updates: Developing mechanisms to incrementally update existing claraprints with new recordings, reducing the computational overhead of regenerating fingerprints from scratch.
Scalable Infrastructure: Deploying the system on scalable infrastructure that can handle the computational demands of processing and matching claraprints in a large-scale environment.
Query Optimization: Fine-tuning the query algorithms and similarity metrics to ensure fast and accurate retrieval of matching claraprints, especially when dealing with a massive volume of data.
By implementing these optimizations, the claraprint system can efficiently handle the complexities of large classical music catalogs, providing reliable cover detection and retrieval capabilities.