ATEPP & Maestro Datasets in AI Research

• ATEPP and Maestro datasets are key AI resources featuring high-quality audio–MIDI pairings and diverse annotations across music and multimodal applications.
• They enable precise musical performance synthesis, sound event detection, and hardware-efficient neural network accelerator design through robust alignment and innovative metrics.
• Their construction, evaluation, and benchmarking methodologies drive reproducible research and offer substantial impacts across music, audio, and scientific modeling domains.

ATEPP and Maestro datasets refer to two influential resources in contemporary AI research and applications, particularly in the domains of music performance synthesis, sound event detection, multimodal representation learning, neural network accelerator design, and exoplanet atmospheric modeling. Each “Maestro” resource arises from distinct research traditions, and the acronym “ATEPP” is currently most prominent in the context of piano performance audio–MIDI research. This entry details their construction, technical usage, evaluation metrics, and impact across these fields.

1. Definitions and Scope

ATEPP (generally “Annotated Transcribed Expressive Piano Performances”; Editor's term) is a large-scale dataset of paired expressive piano performance MIDI and audio, comprising approximately 700 hours from 1,099 albums performed by 46 pianists across 25 composers. It emphasizes rich diversity in both musical style and recording environment, and is intended for data-intensive models in music information retrieval and synthesis (Tang et al., 11 Jul 2025).

The “MAESTRO” datasets and tools span several domains:

• MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization): A corpus of >172 hours of high-fidelity, performance-aligned audio–MIDI from virtuosic piano recitals (Hawthorne et al., 2018).
• MAESTRO (DNN Dataflow Modeling / Accelerator Efficiency): An analytical tool and modeling framework for evaluating the efficiency, reuse, and hardware cost of different deep neural network dataflows (Kwon et al., 2018).
• MAESTRO (Matched Speech Text Representations): A self-supervised learning algorithm for aligning and unifying speech and text embeddings for applications in ASR and speech translation (Chen et al., 2022).
• MAESTRO (Opacity Database for Exoplanet Science): A database and resource of molecular opacities with standardized methods for line wing cut-off in radiative transfer (Ehsan et al., 5 Jan 2024).
• MAESTRO (Sound Event Detection Dataset; DCASE Challenge): “MAESTRO Real” is a strongly-annotated, heterogeneous dataset of sound events, used alongside DESED for recent advances in polyphonic sound event detection (Schmid et al., 17 Jul 2024).

2. Construction, Annotation, and Alignment Principles

ATEPP

ATEPP is constructed by transcribing a broad corpus of commercial piano recordings into MIDI–audio pairs. Diversity across pianists, compositions, and acoustic spaces is a central feature. High-quality alignment of MIDI with audio ensures data suitability for supervised expressive performance synthesis and evaluation (Tang et al., 11 Jul 2025).

MAESTRO (Piano Music Dataset)

MAESTRO comprises >172 hours of performance-level audio from Disklavier-equipped real piano competitions, paired with MIDI data exhibiting fine temporal alignment (~3 ms accuracy) (Hawthorne et al., 2018). The alignment quality permits precise neural modeling of note onsets, offsets, and dynamics.

MAESTRO (Sound Event Detection)

The MAESTRO Real dataset delivers strong temporal labels for sound event detection and is specifically annotated for heterogeneity and granularity to complement datasets like DESED in DCASE 2024 (Schmid et al., 17 Jul 2024).

MAESTRO (Speech-Text, Opacity, DNN Dataflow)

Construction for these resources is domain-specific: in speech–text matching, paired and unpaired sequences are used to train models for latent-space alignment (Chen et al., 2022); in opacity, spectra are simulated with pressure/temperature-varied line lists (Ehsan et al., 5 Jan 2024); in DNN accelerator analysis, the input “dataset” is a formal, data-centric specification of deep neural network workloads (Kwon et al., 2018).

3. Methodologies and Data Representations

Resource	Main Representation	Alignment/Annotation Character
ATEPP	Audio–MIDI pairs, discrete tokens	Diverse, large, transcribed, paired
MAESTRO	Audio–MIDI pairs, aligned	High-precision, performance-level
MAESTRO (Sound Events)	Audio events, strong labels	Multigranular, heterogeneous
MAESTRO (Speech-Text)	Latent embeddings, sequence alignment	Paired, with aligned and unpaired
MAESTRO (DNN Dataflow)	Dataflow directives (spatial/temporal maps)	Analytical, formal workload spec
MAESTRO (Opacity)	Spectral lines, wavenumber grid	Standardized cut-off and parameters

In performance synthesis, both ATEPP and MAESTRO encode MIDI and audio as discrete tokens, enabling robust modeling. ATEPP’s diversity improves generalization, while MAESTRO’s fine-grained alignment is optimal for exploiting low-level musical details (Tang et al., 11 Jul 2025, Hawthorne et al., 2018).

Sound event detection pipelines utilizing MAESTRO Real structure annotations to fully exploit cross-label mappings, strong pseudo-labeling, and consistency training (Schmid et al., 17 Jul 2024). In DNN accelerator modeling, MAESTRO’s data-centric directives (Spatial Map, Temporal Map, Cluster) allow precise and compact workload specification, revealing layers of reuse and movement (Kwon et al., 2018).

4. Benchmarking, Metrics, and Technical Evaluation

Major technical metrics for these datasets and their derived models include:

• Frechet Audio Distance (FAD): Used for assessing the realism of synthesized piano audio. MIDI-VALLE achieves over 75% lower FAD on both ATEPP and MAESTRO, reflecting perceptual similarity to human performance (Tang et al., 11 Jul 2025).
• Polyphonic Sound Detection Score (PSDS1): For sound event detection, a PSDS1 of 0.692 demonstrates state-of-the-art single-model performance, achieved using iterative fine-tuning strategies on both MAESTRO and DESED datasets (Schmid et al., 17 Jul 2024).
• Spectrogram/Chroma Distortion: Lower values for MIDI-VALLE indicate improved timbral and harmonic fidelity compared to baselines (Tang et al., 11 Jul 2025).
• Mean Word Error Rate (WER), BLEU: For speech–text representation learning, improvements in these metrics (e.g., 8% relative reduction in WER, +2.8 BLEU for multilingual ST) result from the unified modality alignment in Maestro (Chen et al., 2022).
• Execution Time, Energy, Area Estimates: In MAESTRO’s DNN dataflow modeling, performance is quantified through analytical estimates ($T_{\text{total}}, E_{\text{total}}$) across millions of design configurations (Kwon et al., 2018).
• Spectral Line Wing Cut-off Effects: Differences of up to 50% in computed cross-sections reveal the impact of the adopted cut-off prescription in exoplanet opacity studies (Ehsan et al., 5 Jan 2024).

5. Applications and Impact

Music Performance Synthesis and Analysis

ATEPP and MAESTRO are the canonical datasets for expressive piano performance modeling. MIDI-VALLE, trained on ATEPP and evaluated on both datasets, significantly outperforms prior models, both objectively (FAD) and in listening studies (202 to 58 votes over baseline) (Tang et al., 11 Jul 2025). MAESTRO enables the factorized Wave2Midi2Wave pipeline for joint transcription, composition, and synthesis, advancing modular and interpretable music AI (Hawthorne et al., 2018).

Sound Event Detection

In DCASE 2024, a multi-stage training procedure using both DESED and MAESTRO Real led to new benchmarks. The ensemble and pseudo-labeling strategies improve model reliability, even with label granularity mismatches (Schmid et al., 17 Jul 2024).

Multimodal Representation Learning

Maestro’s method for matched speech–text embeddings establishes stronger transfer and cross-task generalization for low-resource languages in ASR and speech translation (Chen et al., 2022).

Neural Network Accelerator Design

MAESTRO’s dataflow modeling framework and analytical cost model enable fast, accurate hardware-software co-design exploration, with explicit performance–energy–area trade-offs revealed by standardized directives (Kwon et al., 2018).

Exoplanet and Brown Dwarf Atmospheric Modeling

The MAESTRO (Opacity) database adopts a standard wing cut-off prescription:

$$R_{\rm cut,Abs} = \begin{cases} 25~\mathrm{cm}^{-1} & \text{for } P \le 200~\mathrm{bar}, \ 100~\mathrm{cm}^{-1} & \text{for } P > 200~\mathrm{bar} \end{cases}$$

This practice reduces systematic opacity uncertainties and harmonizes comparative studies across ATEPP-like and other databases (Ehsan et al., 5 Jan 2024).

6. Comparative Evaluation and Dataset Interoperability

The diversity of the ATEPP dataset complements the homogeneous, high-fidelity MAESTRO corpus for evaluation and generalization checks in music synthesis (Tang et al., 11 Jul 2025). In sound event detection, class mapping and targeted loss computation resolve heterogeneity in event definitions between MAESTRO Real and DESED (Schmid et al., 17 Jul 2024).

Within atmospheric modeling, harmonized protocols for cross-section calculation (e.g., line wing cut-off) support objective intercomparison and joint benchmarking of MAESTRO and ATEPP-style opacity resources (Ehsan et al., 5 Jan 2024). In energy district optimization, plausible integration of ATEPP-like empirical profiles into simulation frameworks like Maestro (Python) is anticipated for benchmarking predictive control (Gorecki et al., 2019).

7. Significance, Challenges, and Future Directions

ATEPP and MAESTRO datasets, each in their respective research domains, are central to robust benchmarking, model evaluation, and reproducible science. Their adoption has led to measurable advances in performance metrics, generalization, and benchmarking rigor. Key challenges addressed include alignment accuracy, diversity for generalization, annotation heterogeneity, and standardization of processing protocols.

A significant future direction involves dataset interoperability and standard protocols, particularly in spectral and event annotation domains. The emergence of cross-domain pipelines (e.g., from symbolic to audio to event-level tasks) and modular pipelines (as in Wave2Midi2Wave and multi-stage sound event detection) signal increasing value in extensible, well-annotated, and technically rigorous datasets.

A plausible implication is that as more research areas adopt standardized practices (for example, in spectral line wing cut-off), datasets such as MAESTRO and ATEPP will serve as reference points for both technical development and scientific intercomparison, enabling more robust, transparent, and transferable models across domains.