---
title: 'StillSonicSet: Disambiguating Speech & Sonification'
url: https://www.emergentmind.com/topics/stillsonicset
type: topic
---

# StillSonicSet: Disambiguating Speech & Sonification

StillSonicSet is an ambiguous name in recent arXiv literature. In speech enhancement research, it denotes a **synthetic yet acoustically realistic far-field speech dataset** for **stationary-speaker** scenarios, constructed from **LibriSpeech**, **FSD50K**, **FMA**, and **90 Matterport3D scenes**, and used as the principal benchmark for **DiT-Flow**. In a separate sonification paper, the same name denotes a **real-time, EDM-inspired sonification system** for monitoring the Swedish supercomputer **Kebnekaise**. The two usages are technically unrelated except for the shared name, so any rigorous discussion requires explicit disambiguation [2603.21608, 2605.21874].

## 1. Terminological disambiguation

The name has been used for two distinct research artifacts.

| Usage of “StillSonicSet” | Domain | Source |
|---|---|---|
| Synthetic but acoustically realistic far-field speech dataset for stationary speakers | Speech enhancement, acoustic simulation, robust SE benchmarking | [2603.21608] |
| Real-time sonification system for supercomputer monitoring | Sonification, auditory display, HPC monitoring | [2605.21874] |

In the speech-enhancement usage, StillSonicSet is introduced as a response to limitations in prior synthetic corpora, especially simplified room impulse response simulation and the relative underrepresentation of **stationary speakers** in practical settings such as **meetings**, **teleconferencing**, and **classrooms**. In the sonification usage, StillSonicSet is the proposed system for continuous monitoring of an always-running computational environment through indefinitely continuing, stylistically coherent sound [2603.21608, 2605.21874].

## 2. StillSonicSet as a stationary-source speech dataset

In the DiT-Flow paper, StillSonicSet is defined as a **synthetic but acoustically realistic far-field speech dataset** designed for **speech enhancement under multiple distortions**. It is explicitly presented as a variant of **SonicSet** for **stationary speakers**, rather than for moving sound sources. The dataset is composed of **LibriSpeech** for speech, **FSD50K** for noise and environmental sounds, **FMA** for music, and **90 Matterport3D scenes** for acoustic simulation [2603.21608].

Its motivating claim is that many earlier synthetic speech-enhancement datasets rely on acoustically simplified simulation, especially **shoebox-style** or **image-source-method** assumptions, and therefore do not adequately represent **complex room geometries**, **varied surface materials**, or **natural occlusions** such as furniture and architectural structure. StillSonicSet is intended to narrow that realism gap while retaining the scale advantages of synthetic data. The target use case is not generic source motion, but the stationary-source conditions common in telecommunication and meeting scenarios [2603.21608].

The distortions directly modeled within StillSonicSet are **reverberation**, **background noise/music**, and **Opus codec compression**. The paper treats codec artifacts as a distinct source of degradation because practical communication pipelines often involve low-delay compression before enhancement. This makes StillSonicSet not merely a reverberant-speech corpus, but a benchmark for **compound distortions** [2603.21608].

## 3. Construction, simulation lineage, and acoustic ingredients

StillSonicSet is derived from the earlier **SonicSet/SonicSim** framework, which was introduced for **moving sound source scenarios** using **LibriSpeech**, **FSD50K**, **FMA**, and **90 scenes from Matterport3D**. SonicSim itself is a customizable simulation platform based on **Habitat-sim**, intended to model indoor acoustics with **occlusion**, **complex room geometry**, **heterogeneous material properties**, and source motion [2410.01481].

Within that lineage, StillSonicSet reuses the **RIRs provided in SonicSet** and converts moving-source simulation into a stationary-source benchmark by **discretizing the moving RIR trajectories** and taking responses at **fixed positions**. For each scene, the source and microphone placements are randomly generated, and the initial position of each speech source and the position of noise sources are placed within a **1–8 meter radius** of the microphone. To simulate stationary speakers, each speech utterance is convolved with the RIR from **one fixed position only**, and the **volume is not normalized across positions**, so level changes follow source–microphone distance naturally [2603.21608].

The speech component is assembled by randomly selecting **three speech utterances from different speakers** from **LibriSpeech**, **downsampling them to 8 kHz**, and convolving them with **three distinct RIRs**. Background noise and music are randomly selected from **FSD50K** and **FMA** and convolved with **other RIRs from the same acoustic environment**, so speech and non-speech share **consistent spatial characteristics** within a scene. The codec component is added using **Opus** through the **opuslib** Python package, with **8 kHz** audio, **16-bit precision**, **20 ms** encoding frames, encoder complexity **10**, and target bitrate randomly sampled from **30 kbps to 40 kbps** [2603.21608].

The realism claim is therefore architectural rather than documentary: StillSonicSet is not a real-recorded corpus, but a synthetic corpus whose room simulation incorporates **complex geometry**, **surface-material heterogeneity**, **occlusions**, **distance-dependent level variation**, and **scene-consistent spatialization** [2603.21608].

## 4. Benchmark protocol and dataset scale

StillSonicSet serves as the primary dataset for the core DiT-Flow experiments. The paper reports experiments on **two versions** of the dataset: one containing **only reverberant speech**, and another augmented with **noise and compression distortions**. The benchmark evaluations are reported under four conditions: **Reverb only**, **Noise only**, **Reverb + Noise**, and **Reverb + Noise + Codec-Compression**. The last condition is intended to emulate realistic teleconferencing pipelines by applying codec compression after reverberation and additive interference [2603.21608].

For the main experiments, the reported splits are:

- **training**: **50,000 utterances**, approximately **90 hours**
- **validation**: **8 hours**
- **test**: **8 hours**

The evaluation metrics reported on StillSonicSet are **PESQ**, **ESTOI**, **LSD**, **DNSMOS P.835** (**SIG**, **BAK**, **OVRL**), and **speaker similarity** based on **WavLM-based cosine similarity**. The benchmark is therefore designed to assess intelligibility, spectral fidelity, perceptual quality, background suppression, and speaker preservation within the same experimental setting [2603.21608].

This benchmark design distinguishes StillSonicSet from a purely reverberant or purely noisy corpus. The dataset is used to test robustness not only to single distortions but also to the interaction between reverberation, interference, and codec artifacts.

## 5. Reported empirical role in DiT-Flow

StillSonicSet is used to compare **SGMSE**, **StoRM**, and **DiT-Flow** under matched training and evaluation conditions. The most emphasized condition is **Reverb + Noise + Codec-Compression**, which the paper presents as the hardest and most realistic teleconferencing-like setup. In that condition, the **noisy input** has **PESQ 1.126**, **ESTOI 0.312**, **LSD 8.293**, **SIG 1.545**, **BAK 1.494**, **OVRL 1.277**, and **speaker similarity 0.779**. **DiT-Flow** improves these to **1.389**, **0.458**, **4.506**, **3.301**, **3.723**, **2.906**, and **0.880**, respectively, while **StoRM** remains higher on **BAK** at **3.969** [2603.21608].

The paper’s broader conclusion is that DiT-Flow is the most robust model under **multi-distortion conditions**, especially in the compound setting combining reverberation, noise, and codec compression. It does not dominate every metric in every condition, but it is reported as providing the strongest overall balance across **signal naturalness**, **intelligibility**, **spectral fidelity**, and **speaker preservation** [2603.21608].

StillSonicSet’s significance is reinforced by transfer experiments to real-recorded datasets. For **LibriCSS**, DiT-Flow trained on **WSJ0+Reverb** yields **SIG 2.938**, **BAK 3.902**, **OVRL 2.476**, and **Spk Sim 0.917**, whereas training on **StillSonicSet** yields **SIG 2.935**, **BAK 3.950**, **OVRL 2.503**, and **Spk Sim 0.928**. For **RealMAN**, the corresponding comparison is **2.754 / 3.413 / 2.184 / 0.885** versus **2.911 / 3.684 / 2.402 / 0.896**. The larger gains on RealMAN are interpreted in the paper as evidence that StillSonicSet better matches complex real acoustics than the simpler synthetic reverberation baseline [2603.21608].

## 6. Scope boundaries, common misreadings, and the separate sonification usage

Several boundaries are important. First, StillSonicSet is **synthetic**, not real-recorded. Its claim is **“synthetic yet acoustically realistic,”** not documentary realism. Second, it should not be conflated with **SonicSet** itself: SonicSet is a **moving-source** benchmark, whereas StillSonicSet is a **stationary-source** adaptation derived from the same simulation lineage. Third, the five unseen distortions discussed elsewhere in the DiT-Flow paper—**clipping**, **bandwidth limitation**, **codec loss (MP3/OGG)**, **packet loss**, and **wind noise**—belong to the **URGENT adaptation study**, not to StillSonicSet itself [2603.21608].

A further source of confusion is the unrelated sonification paper that uses the same name. In that work, StillSonicSet is not a speech dataset at all, but a system for **real-time sonification of the activity of the Swedish supercomputer Kebnekaise**. It monitors data exposed by **Slurm**, specifically `\procs`, `\memusage`, and `\IB-tx`, receives a new batch every **15 seconds**, maps the machine’s **10 partitions** to **10 layers** of an EDM texture, and uses a **round-robin** foregrounding strategy to manage masking and cognitive overload. That StillSonicSet is a monitoring sonification environment rather than a speech-enhancement benchmark [2605.21874].

The shared name therefore denotes two different objects in two different subfields. In the speech literature, StillSonicSet refers to a **stationary-source, acoustically realistic synthetic benchmark** for far-field speech enhancement under **reverberation, background noise/music, and Opus codec compression**. In sonification, it refers to a **continuous auditory display system** for supercomputer activity. The two usages should be cited and interpreted separately [2603.21608, 2605.21874].

Source: https://www.emergentmind.com/topics/stillsonicset