SKU: 93540706983

Sennheiser HME 27 Broadcast Headset, Electret Mic

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Description

Sennheiser HME 27 Broadcast Headset, Electret MicSennheiser HME 27 Professional Circumaural Broadcast Headset The Sennheiser HME 27 (Current Article Number: 700323, Legacy: 506977) is a premium, reference grade closed back circumaural (over ear) broadcast headset designed specifically for television announcers, radio personalities, and master control operators. The "E" designation within the 27 series family specifies its core hardware change: it replaces the dynamic microphone capsule with a high

Sennheiser HME 27 Professional Circumaural Broadcast Headset

The Sennheiser HME 27 (Current Article Number: 700323, Legacy: 506977) is a premium, reference-grade closed-back circumaural (over-ear) broadcast headset designed specifically for television announcers, radio personalities, and master control operators.

The "E" designation within the 27-series family specifies its core hardware change: it replaces the dynamic microphone capsule with a high-output, permanently polarized electret condenser capsule. This gives the headset a wide frequency range and excellent transient response, matching the studio sound quality of a standalone condenser microphone while keeping the hands-free convenience of a boomset.

Key Acoustic and Operational Attributes

  • Pre-Polarized BKE 4-4 Condenser Capsule: Outfitted with an elite condenser element optimized specifically for vocal clarity and crisp speech presentation. It delivers an extended frequency response ($40\text{ Hz}$ to $20,000\text{ Hz}$) with an upper-mid presence lift that ensures voice translation remains distinct, even when mixed against loud ambient background elements.

  • Highly Directional Cardioid Pattern: The microphone boom utilizes a precise cardioid pickup arc. It isolates the presenter's voice while rejecting room echoes and ambient off-axis spill, minimizing the risk of acoustic feedback loop issues near studio monitor speakers.

  • Onboard Switchable ActiveGard Limiter: Features an analog, mechanical switch-controlled ActiveGard circuit. When toggled on, it acts as an automatic safety buffer that tames unexpected incoming signal bursts over 110 dB SPL to shield the operator's hearing.

  • Pressure-Free Two-Piece Split Headband: Uses an automatically opening split headband structure that spreads apart to shift pressure away from the sensitive areas of the skull. A wide, solid padding wrap is also included in the box for engineers who prefer a traditional single-band profile.

  • Modular Multi-Pin Connection Base: Ships from the factory with an un-cabled connector socket. This modular design interfaces with Sennheiser's Cable-II series (such as the X3K1 XLR/1/4" combo cable), allowing technicians to replace damaged lines instantly or swap connection formats in the field.

Technical Specifications Matrix

Feature Parameter Hardware Specification Profile
Model Reference Name HME 27
Official Article Numbers 700323 (Current Reference) / 506977 (Legacy)
Global Trade Product Code (UPC) 615104390300
Headphone Design / Coupling Dynamic, Closed-Back / Circumaural (Around-the-Ear)
Headphone Nominal Impedance 64 Ohms (Stereo Configuration)
Headphone Frequency Response 8 Hz to 18,000 Hz
Microphone Transducer Type Pre-Polarized Electret Condenser (BKE 4-4 Module)
Microphone Pickup Pattern Cardioid
Microphone Frequency Response 40 Hz to 20,000 Hz
Maximum Sound Pressure Handling 128 dB SPL (at 1 kHz, THD 1%)
Microphone Operating Voltage Range 5V to 15V DC Bias
ActiveGard Safety Threshold Suppresses audio peaks exceeding 110 dB SPL
Net Structural Weight (No Cable) Approx. 240 grams (8.46 oz)

In the Box: Production Package Inventory

  • 1 x Sennheiser HME 27 Broadcast Headset (Electret)

  • 1 x Broad Single Headband Converting Cushion

  • 1 x High-Density Wind and Pop Screen

  • 1 x HZS 11 Cable Attachment Anchor Clip

  • 1 x Quick Start Technical Guide

  • (Note: Connection cables are sold separately according to studio infrastructure needs).

Field Configuration and Power Management Advice

Powering the Electret Condenser Capsule: Unlike standard dynamic headsets that run passively, the HME 27's condenser microphone capsule requires operating power to function. If you are deploying this headset using the standard Cable II-X3K1 connector line hookup, ensure that P12 to P48 Phantom Power is switched on at your console, field recorder, or intercom station. If phantom power is turned off on that channel strip, the headphones will still receive monitoring audio perfectly, but the microphone capsule will not output any signal.

Precise Mechanical Boom Adjustments: The flexible microphone boom arm can be swiveled 270 degrees, allowing the headset to be worn with the microphone extending from either the left or right ear cup. For clean vocal tracking without air interference, use the flexible middle portion of the boom to place the capsule roughly 2 centimeters out from the side of your mouth, ensuring the mic grille faces toward your lips rather than straight ahead. This prevents sudden breath bursts from causing low-frequency vocal popping on your broadcast line.

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SKU: 93540706983

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0x00000000:00000000
Dallas, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
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Zygerian99
Port Orchard, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
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Shannon
Battle Creek, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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Verified Purchase
William P Ross
Belleville, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Adam
Cuba, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026

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