🕷️ Crawler Inspector

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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 61 (from laksa098)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa061.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, meta_canonical AS meta_canonical, ml_categories_json AS ml_categories_json, ml_types_json AS ml_types_json, ml_intent_types_json AS ml_intent_types_json, meta_language AS meta_language, attrs_author AS attrs_author, ifNull(toUnixTimestamp(attrs_publish_time), 0) AS attrs_publish_time, ifNull(toUnixTimestamp(attrs_original_publish_time), 0) AS attrs_original_publish_time, ifNull(attrs_is_republished, 0) AS attrs_is_republished, ifNull(attrs_nr_words, 0) AS attrs_nr_words, ifNull(attrs_boilerpipe_nr_words, 0) AS attrs_boilerpipe_nr_words, ifNull(body_ext_links_number, 0) AS body_ext_links_number, ifNull(body_int_links_number, 0) AS body_int_links_number, ifNull(meta_nofollow, 0) AS meta_nofollow, ifNull(meta_noarchive, 0) AS meta_noarchive, ifNull(props_was_rendered, 0) AS props_was_rendered, ifNull(src_redirect, \'\') AS src_redirect, ifNull(download_time_msec, 0) AS download_time_msec, ifNull(download_ttfb_msec, 0) AS download_ttfb_msec, ifNull(download_size, 0) AS download_size FROM crawler3.page_info_local FINAL PREWHERE int_partition_id = 91 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://papers.cool/arxiv/2509.07756\')), getAhrefsUnparsedNoserviceFromURL(\'https://papers.cool/arxiv/2509.07756\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/papers.cool\/arxiv\/2509.07756","crawl_time":1780430131,"first_indexed_time":1757597615,"http_code":200,"src_unparsed":"cool,papers!\/arxiv\/2509.07756 s443","src_root_hash":"17309916099783778261","history_drop_reason":null,"meta_title":"Spectral and Rhythm Feature Performance Evaluation for Category and Class Level Audio Classification with Deep Convolutional Neural Networks | Cool Papers - Immersive Paper Discovery","meta_descriptions":["Next to decision tree and k-nearest neighbours algorithms deep convolutional neural networks (CNNs) are widely used to classify audio data in many domains like music, speech or environmental sounds. To train a specific CNN various spectral and rhythm features like mel-scaled spectrograms, mel-frequency cepstral coefficients (MFCC), cyclic tempograms, short-time Fourier transform (STFT) chromagrams, constant-Q transform (CQT) chromagrams and chroma energy normalized statistics (CENS) chromagrams can be used as digital image input data for the neural network. The performance of these spectral and rhythm features for audio category level as well as audio class level classification is investigated in detail with a deep CNN and the ESC-50 dataset with 2,000 labeled environmental audio recordings using an end-to-end deep learning pipeline. The evaluated metrics accuracy, precision, recall and F1 score for multiclass classification clearly show that the mel-scaled spectrograms and the mel-frequency cepstral coefficients (MFCC) perform significantly better then the other spectral and rhythm features investigated in this research for audio classification tasks using deep CNNs."],"meta_canonical":null,"ml_categories_json":"","ml_types_json":"","ml_intent_types_json":"","meta_language":null,"attrs_author":null,"attrs_publish_time":0,"attrs_original_publish_time":1757597615,"attrs_is_republished":0,"attrs_nr_words":"318","attrs_boilerpipe_nr_words":"189","body_ext_links_number":8,"body_int_links_number":7,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":0,"src_redirect":"","download_time_msec":886,"download_ttfb_msec":665,"download_size":16865}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📍

LOCATION

Host 61 · Partition 91

laksa061

17309916099783778261

📄

INDEXABLE

✅

CRAWLED

11 hours ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://papers.cool/arxiv/2509.07756
Last Crawled	2026-06-02 19:55:31 (11 hours ago)
First Indexed	2025-09-11 13:33:35 (8 months ago)
HTTP Status Code	200
Content
Meta Title	Spectral and Rhythm Feature Performance Evaluation for Category and Class Level Audio Classification with Deep Convolutional Neural Networks \| Cool Papers - Immersive Paper Discovery
Meta Description	Next to decision tree and k-nearest neighbours algorithms deep convolutional neural networks (CNNs) are widely used to classify audio data in many domains like music, speech or environmental sounds. To train a specific CNN various spectral and rhythm features like mel-scaled spectrograms, mel-frequency cepstral coefficients (MFCC), cyclic tempograms, short-time Fourier transform (STFT) chromagrams, constant-Q transform (CQT) chromagrams and chroma energy normalized statistics (CENS) chromagrams can be used as digital image input data for the neural network. The performance of these spectral and rhythm features for audio category level as well as audio class level classification is investigated in detail with a deep CNN and the ESC-50 dataset with 2,000 labeled environmental audio recordings using an end-to-end deep learning pipeline. The evaluated metrics accuracy, precision, recall and F1 score for multiclass classification clearly show that the mel-scaled spectrograms and the mel-frequency cepstral coefficients (MFCC) perform significantly better then the other spectral and rhythm features investigated in this research for audio classification tasks using deep CNNs.
Meta Canonical	null
Boilerpipe Text	heavy column, fetched on demand
Markdown	heavy column, fetched on demand
Readable Markdown	heavy column, fetched on demand
ML Classification
ML Categories	null
ML Page Types	null
ML Intent Types	null
Content Metadata
Language	null
Author	null
Publish Time	not set
Original Publish Time	2025-09-11 13:33:35 (8 months ago)
Republished	No
Word Count (Total)	318
Word Count (Content)	189
Links
External Links	8
Internal Links	7
Technical SEO
Meta Nofollow	No
Meta Noarchive	No
JS Rendered	No
Redirect Target	null
Performance
Download Time (ms)	886
TTFB (ms)	665
Download Size (bytes)	16,865
Location
Host ID	61 (laksa061)
Partition ID	91
Root Hash	17309916099783778261
Unparsed URL	cool,papers!/arxiv/2509.07756 s443