RRepoGEO

REPOGEO REPORT · LITE

breizhn/DTLN

Default branch master · commit 1de1f15a · scanned 6/10/2026, 2:58:20 AM

GitHub: 718 stars · 173 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface breizhn/DTLN, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening sentence to emphasize deep learning and embedded use

    Why:

    CURRENT
    Tensorflow 2.x implementation of the stacked dual-signal transformation LSTM network (DTLN) for real-time noise suppression.
    COPY-PASTE FIX
    DTLN is a highly efficient, real-time deep learning model for speech denoising and noise suppression, implemented in Tensorflow 2.x. It is specifically optimized for resource-constrained environments and embedded systems like the Raspberry Pi, offering state-of-the-art performance.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., project page, demo, or paper link) to the 'Homepage' field in the repository settings.
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison' or 'Why DTLN?' that briefly outlines DTLN's advantages (e.g., deep learning approach, efficiency, real-time, TensorFlow ecosystem) compared to other solutions like traditional DSP methods (e.g., SpeexDSP, WebRTC) or other deep learning architectures (e.g., DPRNN-TasNet) for specific use cases.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface breizhn/DTLN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RNNoise
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RNNoise · recommended 2×
  2. DPRNN-TasNet · recommended 1×
  3. Conv-TasNet · recommended 1×
  4. SEGAN · recommended 1×
  5. MetricGAN · recommended 1×
  • CATEGORY QUERY
    What are effective deep learning methods for real-time speech enhancement and noise reduction?
    you: not recommended
    AI recommended (in order):
    1. DPRNN-TasNet
    2. Conv-TasNet
    3. SEGAN
    4. MetricGAN
    5. MetricGAN+
    6. Microsoft's DNS-Challenge Baseline
    7. WaveNet
    8. WaveRNN
    9. RNNoise

    AI recommended 9 alternatives but never named breizhn/DTLN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient audio noise suppression model suitable for embedded systems deployment.
    you: not recommended
    AI recommended (in order):
    1. RNNoise
    2. SpeexDSP
    3. WebRTC Audio Processing
    4. DeepSpeech
    5. OpenVINO
    6. TensorFlow Lite

    AI recommended 6 alternatives but never named breizhn/DTLN. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of breizhn/DTLN?
    pass
    AI named breizhn/DTLN explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts breizhn/DTLN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named breizhn/DTLN explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo breizhn/DTLN solve, and who is the primary audience?
    pass
    AI named breizhn/DTLN explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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breizhn/DTLN — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite