What does it mean to deconvolve the impulse response
A linear time-invariant (LTI) system is completely characterized by its impulse response $h(t)$. The output signal $y(t)$ given an arbitrary input signal $x(t)$ is given by the convolution of this input signal with the system's impulse response:

$$y(t)=(x\star h)(t)\tag{1}$$

where $\star$ denotes convolution. In the frequency domain, Eq. (1) becomes a simple multiplication:

$$Y(f)=X(f)H(f)$$

where $Y(f)$, $X(f)$, and $H(f)$ are the Fourier transforms of the signals $y(t)$, $x(t)$, and $h(t)$, respectively.

Deconvolution now refers to the process of "undoing" the effect of an LTI system. I.e. you're given the signal $y(t)$ and you want to recover the original signal $x(t)$. E.g., you might want to remove the reverberation added to a signal by a given room. In this case you need to undo the effect of the convolution of the room's impulse response with the original signal.

Naive deconvolution can in principle be done by applying an inverse filter to the signal $y(t)$. The inverse filter ideally has a frequency response $G(f)=1/H(f)$. Note that such a filter does not necessarily exist, e.g. if $H(f)$ has zeros at certain frequencies. Another disadvantage of this naive deconvolution is the effect it has on measurement noise. In frequency regions where $H(f)$ has a small magnitude, the magnitude of $G(f)$ becomes large, and the noise is highly amplified. This is why applying an inverse filter is usually not a good solution. If the system's impulse response is known and if the noise can be modeled as (wide-sense) stationary, Wiener deconvolution can be used. In that case, the original signal is estimated using a mean square error criterion, which takes into account the impulse response as well as the noise. If the impulse response is unknown (or can't be estimated), there are methods available referred to as blind deconvolution.