diff --git a/CLAUDE.md b/CLAUDE.md
index 588f369..ae979f6 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -6,13 +6,13 @@
 
 ## Project
 
-`noisekit` is a `uvx`-compatible Python CLI that generates degraded speech datasets from clean HuggingFace corpora. It simulates seven atomic audio degradation scenarios — telecom (G.711 calls), low-bitrate codec compression, noisy environments (real ambient noise), far-field reverb, transmission dropout, and clipping distortion — plus compound multi-condition scenarios built by chaining atomic presets. Designed for ASR noise-robustness benchmarking. A `clean_reference` control completes the catalog.
+`noisekit` is a `uvx`-compatible Python CLI that generates degraded speech datasets from clean HuggingFace corpora. It simulates six atomic audio degradation scenarios — telecom (G.711 calls), low-bitrate codec compression, noisy environments (real ambient noise), far-field reverb, and clipping distortion — plus compound multi-condition scenarios built by chaining atomic presets. Designed for ASR noise-robustness benchmarking. A `clean_reference` control completes the catalog.
 
 ## Package Management
 
 Use **UV** for everything: `uv add`, `uv run`, `uv sync`. Never use pip directly.
 
-Key runtime dependencies: `audiomentations>=0.38`, `lameenc>=1.4` (pure-Python MP3 encoder used by `Mp3Compression` in `telecom` and `low_bitrate`; no system ffmpeg needed), `torchmetrics>=1.7.0` (NISQA scoring — downloads ~50 MB model weights to `~/.torchmetrics/NISQA/` on first use), `pyroomacoustics` (room acoustics simulation for `reverb_far_field` — now a core dependency, no extra install needed).
+Key runtime dependencies: `audiomentations>=0.38`, `lameenc>=1.4` (pure-Python MP3 encoder used by `Mp3Compression` in `telecom` and `low_bitrate`; no system ffmpeg needed), `torchmetrics>=1.7.0` (NISQA scoring — downloads ~50 MB model weights to `~/.torchmetrics/NISQA/` on first use), `pyroomacoustics` (room acoustics simulation for `reverb` — now a core dependency, no extra install needed).
 
 ## Architecture
 
@@ -23,7 +23,7 @@ noisekit/
 ├── dataset.py      # HuggingFace dataset loading (soundfile decoder, no torchcodec)
 ├── transforms.py   # Preset loading; returns PresetTransforms(full, scoring, scoring_sr)
 ├── scoring.py      # PESQ + SNR + NISQA; PESQ NB at 8 kHz for telephony presets
-├── noise_cache.py  # Auto-downloads MUSAN music+noise for noisy_environment
+├── noise_cache.py  # Auto-downloads MUSAN music+noise for noise
 └── presets/        # YAML preset files bundled with the package
 
 ```
@@ -32,7 +32,7 @@ noisekit/
 
 ```bash
 noisekit generate --dataset <hf-name> --samples N --presets P1 P2 --output ./out --seed 42
-noisekit generate ... --presets noisy_environment --noise-dir /path/to/noise_wavs
+noisekit generate ... --presets noise --noise-dir /path/to/noise_wavs
 noisekit generate ... --no-nisqa          # skip NISQA (no model download, faster)
 noisekit score ./audio_dir [--reference-dir ./ref] [--output scores.json]
 noisekit score ./audio_dir --no-nisqa     # skip NISQA for standalone scoring
@@ -41,7 +41,7 @@ noisekit list-presets [--verbose]
 
 Custom presets: `--preset-file ./my_preset.yaml`
 
-The `noisy_environment` preset uses a directory of background-noise WAVs. If `--noise-dir` is omitted, noisekit auto-downloads a small MUSAN **noise-only** subset (~20 files, ~120 MB) from `Aynursusuz/musan-audio-dataset` on HuggingFace to `~/.cache/noisekit/noise/musan_ambient/` on first use. Both `speech` and `music` classes are excluded: speech pollutes ASR/PESQ scoring; music sounds artificial as a background and is indistinguishable from white noise at low levels. Only label 2 (`noise` — wind, rain, traffic, machinery) is downloaded.
+The `noise` preset uses a directory of background-noise WAVs. If `--noise-dir` is omitted, noisekit auto-downloads a small MUSAN **noise-only** subset (~20 files, ~120 MB) from `Aynursusuz/musan-audio-dataset` on HuggingFace to `~/.cache/noisekit/noise/musan_ambient/` on first use. Both `speech` and `music` classes are excluded: speech pollutes ASR/PESQ scoring; music sounds artificial as a background and is indistinguishable from white noise at low levels. Only label 2 (`noise` — wind, rain, traffic, machinery) is downloaded.
 
 Pass `--noise-dir /path/to/wavs` to use your own corpus (e.g. MUSAN, DEMAND, FSD50K) instead. Inside a preset YAML, use the literal string `${NOISE_DIR}` as a parameter value and `transforms.load_preset` substitutes the resolved path at load time. Auto-download is wired in `pipeline.run_generate` via `noise_cache.ensure_default_noise_dir()`, gated by `transforms.preset_requires_noise_dir()`.
 
@@ -70,10 +70,9 @@ Built-in presets:
 | `clean_reference`      | Minimal gain normalization (PESQ ceiling)             | full                | WB 16 kHz | 4.0-4.5    |
 | `telecom`              | G.711 call + low-bitrate MP3 codec artifacts          | 300-3400 Hz @ 8 kHz | NB 8 kHz  | 2.0-3.5    |
 | `low_bitrate`    | Wideband low-bitrate MP3 compression (16-32 kbps)     | 80-7500 Hz @ 16 kHz | WB 16 kHz | 1.5-2.5    |
-| `noisy_environment`    | Real ambient noise via `AddBackgroundNoise`           | up to 8-12 kHz      | WB 16 kHz | 2.0-3.5    |
-| `clipping_distortion`  | Microphone overload / ADC saturation (`ClippingDistortion` 10-25%) | full | WB 16 kHz | 2.0-3.5    |
-| `transmission_dropout` | VoIP packet loss: 1-3 silent dropout windows          | full                | WB 16 kHz | 1.5-3.0    |
-| `reverb_far_field`     | Far-field reverberant room via `RoomSimulator`                              | full | WB 16 kHz | 2.0-3.5 |
+| `noise`                | Real ambient noise via `AddBackgroundNoise`           | up to 8-12 kHz      | WB 16 kHz | 2.0-3.5    |
+| `clipping`             | Microphone overload / ADC saturation (`ClippingDistortion` 10-25%) | full | WB 16 kHz | 2.0-3.5    |
+| `reverb`               | Far-field reverberant room via `RoomSimulator`                              | full | WB 16 kHz | 2.0-3.5 |
 
 `telecom` and any compound preset ending with `telecom` use the 8 kHz PESQ NB scoring split (see below). All other presets score in PESQ WB at 16 kHz.
 
@@ -83,9 +82,9 @@ Compound presets chain two or more atomic presets together. Noise is added first
 
 | Preset             | Chain                                     | Requires      | PESQ mode | Target MOS |
 | ------------------ | ----------------------------------------- | ------------- | --------- | ---------- |
-| `noisy_telecom`    | `noisy_environment` → `telecom`           | `--noise-dir` | NB 8 kHz  | 1.5-2.5    |
-| `reverb_noisy`     | `reverb_far_field` → `noisy_environment`  | `--noise-dir` | WB 16 kHz | 1.0-2.5    |
-| `clipping_telecom` | `clipping_distortion` → `telecom`         | —             | NB 8 kHz  | 1.0-2.5    |
+| `noise_telecom`    | `noise` → `telecom`                       | `--noise-dir` | NB 8 kHz  | 1.5-2.5    |
+| `noise_reverb`     | `noise` → `reverb`                        | `--noise-dir` | WB 16 kHz | 1.0-2.5    |
+| `clipping_telecom` | `clipping` → `telecom`                    | —             | NB 8 kHz  | 1.0-2.5    |
 
 ### Compound Preset YAML Format
 
@@ -103,14 +102,14 @@ Rules:
 - `chain` and `transforms` are mutually exclusive.
 - Chained entries must be names of built-in atomic presets (no nesting chains).
 - `${NOISE_DIR}` resolution and the PESQ NB scoring split are detected automatically across the full concatenated chain.
-- `reverb_far_field` uses `pyroomacoustics` (bundled as a core dependency — no extra install needed).
+- `reverb` uses `pyroomacoustics` (bundled as a core dependency — no extra install needed).
 
 ### Why no white noise
 
 The catalog deliberately avoids `AddGaussianSNR` — white Gaussian noise sounds artificial and doesn't reflect real production audio. Instead:
 
 - `telecom` and `low_bitrate` rely on `Mp3Compression` at 16-32 kbps for realistic codec smearing/pre-echo.
-- `noisy_environment` uses `AddBackgroundNoise` over a user-supplied WAV corpus (MUSAN/DEMAND/FSD50K), so the noise floor matches the real environment you care about.
+- `noise` uses `AddBackgroundNoise` over a user-supplied WAV corpus (MUSAN/DEMAND/FSD50K), so the noise floor matches the real environment you care about.
 
 ## PESQ Scoring — Important Design Decision
 
@@ -134,7 +133,7 @@ if peak > 1e-9:
 
 **Safety:** The same normalized `ref_16k` is used as both the transform input and the PESQ/SNR reference, so all quality metrics remain valid relative comparisons. The mid-chain `Normalize` inside `telecom.yaml` (before `BitCrush`) is still needed separately — the bandpass filter removes energy and that step re-normalizes before quantization.
 
-**`noisy_environment` also pre-normalizes:** `noisy_environment.yaml` adds a `Normalize` as its first transform. This handles the `reverb_noisy` compound case: `RoomSimulator` can attenuate the signal by ~10× at large mic distances; without the mid-chain normalize, `AddBackgroundNoise` would see the attenuated level and mix noise too quietly. All compound presets using `noisy_environment` inherit this fix automatically.
+**`noise` also pre-normalizes:** `noise.yaml` adds a `Normalize` as its first transform. This handles the `noise_reverb` compound case: `RoomSimulator` can attenuate the signal by ~10× at large mic distances; without the mid-chain normalize, `AddBackgroundNoise` would see the attenuated level and mix noise too quietly. All compound presets using `noise` inherit this fix automatically.
 
 `transforms.py` auto-detects this split: if the last transform is `Resample(16000)`, it creates a `scoring` Compose (all-but-last) alongside the `full` Compose.
 
@@ -184,19 +183,19 @@ cat test_out/metadata.jsonl
 # New atomic presets — no external dependencies
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
-  --samples 3 --presets clipping_distortion transmission_dropout \
+  --samples 3 --presets clipping \
   --no-nisqa --output ./test_atomic --seed 42
 
-# noisy_environment — auto-downloads MUSAN noise-only clips on first run
+# noise — auto-downloads MUSAN noise-only clips on first run
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
-  --samples 3 --presets noisy_environment \
+  --samples 3 --presets noise \
   --output ./test_noise --seed 42
 
 # Compound presets (auto-downloads MUSAN noise on first run)
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
-  --samples 3 --presets noisy_telecom \
+  --samples 3 --presets noise_telecom \
   --no-nisqa --output ./test_compound --seed 42
 
 # clipping_telecom — no noise dir needed
@@ -208,19 +207,19 @@ uv run noisekit generate \
 # Far-field reverb
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
-  --samples 3 --presets reverb_far_field reverb_noisy \
+  --samples 3 --presets reverb noise_reverb \
   --no-nisqa --output ./test_reverb --seed 42
 
-# noisy_environment with your own noise corpus (skips auto-download)
+# noise with your own noise corpus (skips auto-download)
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
-  --samples 3 --presets noisy_environment \
+  --samples 3 --presets noise \
   --noise-dir ~/datasets/musan/noise \
   --output ./test_noise --seed 42
 ```
 
-Expected PESQ spread: clean ~4.6, telecom ~2.5-3.5 (NB), low_bitrate ~1.5-2.5 (WB), noisy_environment ~1.0-2.5 (WB), clipping_distortion ~2.0-3.5 (WB), transmission_dropout ~1.5-3.0 (WB), reverb_far_field ~2.0-3.5 (WB).
+Expected PESQ spread: clean ~4.6, telecom ~2.5-3.5 (NB), low_bitrate ~1.5-2.5 (WB), noise ~1.0-2.5 (WB), clipping ~2.0-3.5 (WB), reverb ~2.0-3.5 (WB).
 
-Compound preset PESQ: noisy_telecom ~1.5-2.5 (NB), clipping_telecom ~1.0-2.5 (NB), reverb_noisy ~1.0-2.5 (WB).
+Compound preset PESQ: noise_telecom ~1.5-2.5 (NB), clipping_telecom ~1.0-2.5 (NB), noise_reverb ~1.0-2.5 (WB).
 
 Expected NISQA spread: clean ~4.0-4.5, degraded presets ~1.5-3.0. NISQA model weights (~50 MB) are downloaded on first run.
diff --git a/README.md b/README.md
index dc1002e..d9c3f42 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ Generate degraded speech datasets for noise-robust ASR benchmarking.
 
 Takes a clean HuggingFace speech dataset, applies real-world degradation presets via [audiomentations](https://github.com/iver56/audiomentations), and scores each output with PESQ, SNR, and NISQA, producing a JSONL manifest ready for noise-robustness benchmarking.
 
-Seven atomic degradation scenarios are built in: telephony (G.711 + low-bitrate codec), wideband codec compression, ambient noise, clipping distortion, transmission dropout, and far-field reverb. Atomic presets compose into compound multi-condition scenarios.
+Six atomic degradation scenarios are built in: telephony (G.711 + low-bitrate codec), wideband codec compression, ambient noise, clipping distortion, and far-field reverb. Atomic presets compose into compound multi-condition scenarios.
 
 > [!NOTE]
 > Degradations are programmatically simulated. Scores may not generalize to genuine production recordings; validate final benchmarks on annotated real-world data.
@@ -61,12 +61,12 @@ uvx noisekit generate \
   --seed 42
 ```
 
-For `noisy_environment`, supply a directory of real noise WAVs (e.g. [MUSAN](https://www.openslr.org/17/), [DEMAND](https://zenodo.org/record/1227121), or [FSD50K](https://zenodo.org/record/4060432)):
+For `noise`, you can supply your own background-noise WAVs with `--noise-dir` (e.g. [MUSAN](https://www.openslr.org/17/), [DEMAND](https://zenodo.org/record/1227121), or [FSD50K](https://zenodo.org/record/4060432)):
 
 ```bash
 uvx noisekit generate \
   --dataset google/fleurs --config en_us --split test \
-  --samples 300 --presets noisy_environment \
+  --samples 300 --presets noise \
   --noise-dir ~/datasets/musan/noise \
   --output ./benchmark_dataset --seed 42
 ```
@@ -131,7 +131,7 @@ uvx noisekit list-presets --verbose   # show full transform stack
 
 ## Presets
 
-Ten built-in presets: seven atomic scenarios, three compound multi-condition presets, and a clean reference control. None use synthetic white noise; codec artifacts, real ambient recordings, and room simulation produce the degradation instead.
+Nine built-in presets: six atomic scenarios, three compound multi-condition presets, and a clean reference control. None use synthetic white noise; codec artifacts, real ambient recordings, and room simulation produce the degradation instead.
 
 ### Atomic presets
 
@@ -140,26 +140,25 @@ Ten built-in presets: seven atomic scenarios, three compound multi-condition pre
 | `clean_reference`      | Minimal processing (PESQ ceiling / control)                              | 4.0-4.5    |
 | `telecom`              | G.711-style call: 8 kHz bandpass + 8-bit BitCrush + 16-32 kbps MP3 codec | NB 2.0-3.5 |
 | `low_bitrate`    | Wideband audio crushed by 16-32 kbps MP3 compression                     | WB 1.5-2.5 |
-| `noisy_environment`    | Real ambient noise from `--noise-dir` mixed in at SNR 5-15 dB            | WB 1.0-2.5 |
-| `clipping_distortion`  | Microphone overload: clips the loudest 10-25% of samples                 | WB 2.0-3.5 |
-| `transmission_dropout` | VoIP packet loss: 1-3 silent dropout windows (60-180 ms each)            | WB 1.5-3.0 |
-| `reverb_far_field`     | Far-field room reverb at 1-3 m mic distance                              | WB 2.0-3.5 |
+| `noise`                | Real ambient noise from `--noise-dir` mixed in at SNR 5-15 dB            | WB 1.0-2.5 |
+| `clipping`             | Microphone overload: clips the loudest 10-25% of samples                 | WB 2.0-3.5 |
+| `reverb`               | Far-field room reverb at 1-3 m mic distance                              | WB 2.0-3.5 |
 
 `telecom` is scored with PESQ narrowband at 8 kHz (before the final upsample); all other presets are scored wideband at 16 kHz.
 
-All atomic presets require no noise corpus. All dependencies, including `pyroomacoustics` (used by `reverb_far_field`), are bundled with no extra install needed.
+All dependencies, including `pyroomacoustics` (used by `reverb`), are bundled with no extra install needed.
 
-`noisy_environment` requires `--noise-dir` pointing at a directory of background-noise WAVs (e.g. MUSAN, DEMAND, FSD50K). If omitted, noisekit auto-downloads a small MUSAN noise-only subset (~120 MB) from HuggingFace on first use.
+`noise` accepts a `--noise-dir` pointing at a directory of background-noise WAVs (e.g. MUSAN, DEMAND, FSD50K). If omitted, noisekit auto-downloads a small MUSAN noise-only subset (~20 files, ~120 MB) to `~/.cache/noisekit/noise/musan_ambient/` on first use.
 
 ### Compound presets
 
 Compound presets chain two atomic presets together. Noise is applied first (acoustic environment), then codec or dropout (digital processing on the already-degraded signal).
 
-| Preset             | Chain                                    | Requires      | PESQ       |
-| ------------------ | ---------------------------------------- | ------------- | ---------- |
-| `noisy_telecom`    | `noisy_environment` → `telecom`          | `--noise-dir` | NB 1.5-2.5 |
-| `clipping_telecom` | `clipping_distortion` → `telecom`        | (none)        | NB 1.0-2.5 |
-| `reverb_noisy`     | `reverb_far_field` → `noisy_environment` | `--noise-dir` | WB 1.0-2.5 |
+| Preset             | Chain                                    | Noise source                   | PESQ       |
+| ------------------ | ---------------------------------------- | ------------------------------ | ---------- |
+| `noise_telecom`    | `noise` → `telecom`          | `--noise-dir` or auto-download | NB 1.5-2.5 |
+| `clipping_telecom` | `clipping` → `telecom`       | (none)                         | NB 1.0-2.5 |
+| `noise_reverb`     | `noise` → `reverb`           | `--noise-dir` or auto-download | WB 1.0-2.5 |
 
 You can also define your own compound preset with a `chain:` key in a YAML file:
 
@@ -167,7 +166,7 @@ You can also define your own compound preset with a `chain:` key in a YAML file:
 name: my_compound
 description: "Noisy environment then telephony codec"
 chain:
-  - noisy_environment
+  - noise
   - telecom
 ```
 
diff --git a/noisekit/cli.py b/noisekit/cli.py
index bb9c011..3ed524d 100644
--- a/noisekit/cli.py
+++ b/noisekit/cli.py
@@ -34,8 +34,9 @@ def generate(
             "--noise-dir",
             help=(
                 "Directory of background-noise WAVs (e.g. MUSAN, DEMAND, FSD50K). "
-                "Used by noisy_environment. If omitted, a small MUSAN music+noise "
-                "subset is auto-downloaded to ~/.cache/noisekit/ on first use."
+                "Used by the noise preset and compound noise presets. "
+                "If omitted, a small MUSAN noise-only subset (~20 files, ~120 MB) "
+                "is auto-downloaded to ~/.cache/noisekit/noise/musan_ambient/ on first use."
             ),
         ),
     ] = None,
diff --git a/noisekit/noise_cache.py b/noisekit/noise_cache.py
index 4d3fc71..20ea88d 100644
--- a/noisekit/noise_cache.py
+++ b/noisekit/noise_cache.py
@@ -1,4 +1,4 @@
-"""Auto-download and cache MUSAN noise-only clips for noisy_environment.
+"""Auto-download and cache MUSAN noise-only clips for noise.
 
 Only the `noise` class (wind, rain, traffic, machinery…) is downloaded.
 Speech and music are both excluded: speech pollutes ASR/PESQ scoring;
@@ -28,7 +28,7 @@ def get_default_noise_cache_dir() -> Path:
 
 
 def ensure_default_noise_dir(num_samples: int = DEFAULT_NOISE_NUM_SAMPLES) -> Path:
-    """Return a directory of MUSAN music+noise WAVs, downloading on first use."""
+    """Return a directory of MUSAN noise-only WAVs, downloading on first use."""
     cache_dir = get_default_noise_cache_dir()
     cache_dir.mkdir(parents=True, exist_ok=True)
 
diff --git a/noisekit/presets/clipping_distortion.yaml b/noisekit/presets/clipping.yaml
similarity index 92%
rename from noisekit/presets/clipping_distortion.yaml
rename to noisekit/presets/clipping.yaml
index 98285b6..9be9522 100644
--- a/noisekit/presets/clipping_distortion.yaml
+++ b/noisekit/presets/clipping.yaml
@@ -1,4 +1,4 @@
-name: clipping_distortion
+name: clipping
 description: "Amplitude clipping simulating microphone overload or ADC saturation (10–25% of peak samples)."
 transforms:
   - type: ClippingDistortion
diff --git a/noisekit/presets/clipping_telecom.yaml b/noisekit/presets/clipping_telecom.yaml
index b1eebb1..8cfbc86 100644
--- a/noisekit/presets/clipping_telecom.yaml
+++ b/noisekit/presets/clipping_telecom.yaml
@@ -1,5 +1,5 @@
 name: clipping_telecom
 description: "Amplitude clipping over a telephone channel: ADC saturation then G.711-style narrowband codec."
 chain:
-  - clipping_distortion
+  - clipping
   - telecom
diff --git a/noisekit/presets/noisy_environment.yaml b/noisekit/presets/noise.yaml
similarity index 79%
rename from noisekit/presets/noisy_environment.yaml
rename to noisekit/presets/noise.yaml
index 7c7f4cd..cc85acb 100644
--- a/noisekit/presets/noisy_environment.yaml
+++ b/noisekit/presets/noise.yaml
@@ -1,5 +1,5 @@
-name: noisy_environment
-description: "Real-world ambient background noise mixed at variable SNR (5–15 dB). Requires --noise-dir."
+name: noise
+description: "Real-world ambient background noise mixed at variable SNR (5-15 dB). Uses --noise-dir or auto-downloads MUSAN noise on first use."
 transforms:
   - type: Normalize
     parameters: {}
diff --git a/noisekit/presets/noise_reverb.yaml b/noisekit/presets/noise_reverb.yaml
new file mode 100644
index 0000000..5216e53
--- /dev/null
+++ b/noisekit/presets/noise_reverb.yaml
@@ -0,0 +1,5 @@
+name: noise_reverb
+description: "Far-field reverberant room with superimposed ambient background noise. Uses --noise-dir or auto-downloads MUSAN noise on first use."
+chain:
+  - noise
+  - reverb
diff --git a/noisekit/presets/noise_telecom.yaml b/noisekit/presets/noise_telecom.yaml
new file mode 100644
index 0000000..3ea8127
--- /dev/null
+++ b/noisekit/presets/noise_telecom.yaml
@@ -0,0 +1,5 @@
+name: noise_telecom
+description: "G.711 telephone channel with ambient background noise: background noise then narrowband codec. Uses --noise-dir or auto-downloads MUSAN noise on first use."
+chain:
+  - noise
+  - telecom
diff --git a/noisekit/presets/noisy_telecom.yaml b/noisekit/presets/noisy_telecom.yaml
deleted file mode 100644
index eb7b79d..0000000
--- a/noisekit/presets/noisy_telecom.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-name: noisy_telecom
-description: "G.711 telephone channel with ambient background noise: background noise then narrowband codec. Requires --noise-dir."
-chain:
-  - noisy_environment
-  - telecom
diff --git a/noisekit/presets/reverb_far_field.yaml b/noisekit/presets/reverb.yaml
similarity index 95%
rename from noisekit/presets/reverb_far_field.yaml
rename to noisekit/presets/reverb.yaml
index 83b09e6..72f0f7f 100644
--- a/noisekit/presets/reverb_far_field.yaml
+++ b/noisekit/presets/reverb.yaml
@@ -1,4 +1,4 @@
-name: reverb_far_field
+name: reverb
 description: "Far-field recording in a reverberant room via acoustic room simulation (1–3 m mic distance)."
 transforms:
   - type: RoomSimulator
diff --git a/noisekit/presets/reverb_noisy.yaml b/noisekit/presets/reverb_noisy.yaml
deleted file mode 100644
index 56d9d6d..0000000
--- a/noisekit/presets/reverb_noisy.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-name: reverb_noisy
-description: "Far-field reverberant room with superimposed ambient background noise. Requires --noise-dir."
-chain:
-  - reverb_far_field
-  - noisy_environment
diff --git a/noisekit/presets/transmission_dropout.yaml b/noisekit/presets/transmission_dropout.yaml
deleted file mode 100644
index 88c8583..0000000
--- a/noisekit/presets/transmission_dropout.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-name: transmission_dropout
-description: "Simulated packet loss: 1–3 silent dropout windows per utterance."
-transforms:
-  - type: TimeMask
-    parameters:
-      min_band_part: 0.02
-      max_band_part: 0.06
-      fade_duration: 0.002
-    p: 1.0
-  - type: TimeMask
-    parameters:
-      min_band_part: 0.02
-      max_band_part: 0.06
-      fade_duration: 0.002
-    p: 0.8
-  - type: TimeMask
-    parameters:
-      min_band_part: 0.02
-      max_band_part: 0.06
-      fade_duration: 0.002
-    p: 0.5
diff --git a/tests/test_smoke.py b/tests/test_smoke.py
index f77bd2b..bde5813 100644
--- a/tests/test_smoke.py
+++ b/tests/test_smoke.py
@@ -17,17 +17,16 @@ def test_list_builtin_presets() -> None:
     from noisekit.transforms import list_builtin_presets
 
     presets = list_builtin_presets()
-    assert len(presets) == 10
+    assert len(presets) == 9
     names = {p["name"] for p in presets}
     assert "clean_reference" in names
     assert "telecom" in names
     assert "low_bitrate" in names
-    assert "noisy_environment" in names
-    assert "clipping_distortion" in names
-    assert "transmission_dropout" in names
-    assert "reverb_far_field" in names
-    assert "noisy_telecom" in names
-    assert "reverb_noisy" in names
+    assert "noise" in names
+    assert "clipping" in names
+    assert "reverb" in names
+    assert "noise_telecom" in names
+    assert "noise_reverb" in names
     assert "clipping_telecom" in names
 
 
@@ -40,9 +39,9 @@ def test_load_compound_preset_scoring_split(tmp_path) -> None:
     # AddBackgroundNoise scans sounds_path at construction — write a minimal WAV.
     sf.write(tmp_path / "noise.wav", np.zeros(16000, dtype=np.float32), 16000)
 
-    # noisy_telecom chains noisy_environment → telecom.
+    # noise_telecom chains noise → telecom.
     # The concatenated transform list ends with Resample(16000), so the NB 8 kHz
     # scoring split should be detected automatically.
-    pt = load_preset("noisy_telecom", noise_dir=tmp_path)
-    assert pt.scoring is not None, "noisy_telecom should inherit telecom's NB scoring split"
+    pt = load_preset("noise_telecom", noise_dir=tmp_path)
+    assert pt.scoring is not None, "noise_telecom should inherit telecom's NB scoring split"
     assert pt.scoring_sr == 8000, "scoring_sr should be 8000 from telecom's Resample"